Markers associated with human double minute 2 inhibitors

ABSTRACT

The invention provides methods of monitoring differential gene expression of biomarkers to determine patient sensitivity to Human Double Minute inhibitors (MDM2i), methods of determining the sensitivity of a cell to an MDM2i by measuring biomarkers and methods of screening for candidate MDM2i.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of priority to U.S. provisionalapplication Ser. No. 61/677,859, filed Jul. 31, 2012, which is hereinincorporated by reference.

FIELD OF THE INVENTION

The present invention relates to the field of pharmacogenomics, and theuse of biomarkers useful in determining patient sensitivity prior totreatment, following patient response after treatment, cancersensitivity and screening of compounds.

BACKGROUND

p53, also known as tumor protein 53, is a tumor suppressor gene involvedin the prevention of cancer, often referred to as the gatekeeper orguardian of the genome (Levine, Cell 1997, 88:323-331). The p53 geneencodes for a transcription factor that is normally quiescent, andbecoming activated when the cell is stressed or damaged, such as whenDNA damage incurred from a mutagen. If the cell is stressed or damaged,p53 acts to limit the damage, or barring that, trigger the apoptoticpathway so the damaged cell is eliminated and no longer a threat to theorganism (Vogelstein et al., Nature 2000, 408:307-310). An analysis ofdifferent cancers showed that p53 is mutated in about 50% of humancancers (Hollstein et al., Nucleic Acids Res. 1994, 22:3551-3555:Hollstein et al., Science 1991, 253(5015): 49-53). Humans who areheterozygous for p53, with only a single functional copy, will developtumors early in adulthood, a disorder known as Li-Fraumeni syndrome(Varley et al., Hum. Mutat. 2003, 21(3):313-320). However, as much asp53 regulates the cell's fate, p53 is regulated by another protein knownas MDM2.

Double minute 2 protein (MDM2) was discovered as a negative regulator ofp53 (Fakharzadeh et al., EMBO J. 1991, 10(6):1565-1565). MDM2 encodes anE3 ligase containing a p53 binding domain and a nuclear export signalsequence, and upon complexing with p53, removes it from the nucleus andubiquitinylates it, which promotes the degradation of the p53 proteinvia the ubiquitin-proteosome pathway (Haupt et al., Nature 1997,387(6630):296-299; Piette et al., Oncogene 1997 15(9):1001-1010). Inaddition, MDM2 directly inhibits the activity of p53 by binding to thep53 transactivation domain, also preventing p53 mediated gene expression(Wu et a., Genes Dev. 1993, 7:1126-1132). Thus, MDM2 regulates p53 inmultiple ways.

MDM2 is overexpressed in a number of cancers, for example, liposarcoma,glioblastoma, and leukemia (Momand et al., Nucleic Acids Res. 1998,26(15):3453-3459). Overexpression of MDM2 can interfere with theactivities of p53, preventing apoptosis and growth arrest of the tumor(de Rozieres et al., Oncogene 2000, 19(13):1691-1697). Overexpression ofMDM2 correlates with poor prognosis in glioma, and acute lympocyticleukemia (Onel et al., Mol. Cancer Res. 2004, 2(1):1-8).

As MDM2 is an inhibitor of p53, therapeutics which prevent the bindingof MDM2 to p53 would prevent the degradation of p53, allowing free p53to bind and mediate gene expression in cancer cells, resulting in cellcycle arrest and apoptosis. There are previous reports of small moleculeinhibitors of the p53-MDM2 interaction (Vassilev et al., Science, 2004,303(5659):844-888; Zhang et al., Anticancer drugs, 2009 20(6):416-424;Vu et al., Curr. Topics Microbiol. Immuno., 2011, 348:151-172). The modeof binding of these compounds and a crystal structure of the humanMDM2—Nutlin complex as well as a scaffold and pockets of the p53 bindingsite on MDM2 are also known (Vassilev, supra). The first of these MDM2inhibitors, known as the Nutlins, bind MDM2 and occupy the p53 bindingpocket, preventing the formation of the MDM2-p53 complex. This leads toless degradation of the p53 protein, and expression of p53 target genes.Cancer cell lines treated with Nutlins showed growth arrest andincreased apoptosis. For example, the SJSA-1 osteosarcoma line containsamplified copies of the MDM2 gene. Treatment of this line with Nutlin-3reduced proliferation and increased apoptosis (Vassilev et al., Science,2004, 303(5659):844-888). The SJSA-1 cell line was used in creatingxenographs in mouse. Administration of Nutlin-3 reduced xenograft growthby 90%. To investigate the effect the Nutlin compounds had onnon-cancerous cells, human and mouse normal fibroblasts were treatedwith Nutlin-3 and while the proliferation of the cells was slowed, theyretained their viability (Vassilev, supra).

Finding biomarkers which indicate which patient should receive atherapeutic is useful, especially with regard to cancer. This allows formore timely and aggressive treatment as opposed to a trial and errorapproach. In addition, the discovery of biomarkers which indicate thatcells continue to be sensitive to the therapy after administration isalso useful. These biomarkers can be used to monitor the response ofthose patients receiving the therapeutic. If biomarkers indicate thatthe patient has become insensitive to the treatment, then the dosageadministered can be increased, decreased, completely discontinued or anadditional therapeutic administered. As such, there is a need to developbiomarkers associated with MDM2 inhibitors. This approach ensures thatthe correct patients receive the appropriate treatment and during thecourse of the treatment the patient can be monitored for continued MDM2inhibitor sensitivity.

In the development of MDM2 inhibitors, specific biomarkers will aid inunderstanding the mechanism of action upon administration. The mechanismof action may involve a complex cascade of regulatory mechanisms in thecell cycle and differential gene expression. This analysis is done atthe pre-clinical stage of drug development in order to determine theparticular sensitivity of cancer cells to the MDM2 inhibitor candidateand the activity of the candidate. Of particular interest in thepharmacodynamic investigation is the identification of specific markersof sensitivity and activity, such as the ones disclosed herein.

SUMMARY OF THE INVENTION

The invention relates to the analysis that a number of genes identifiedin Table 2 act as specific biomarkers in determining the sensitivity ofcells to MDM2 inhibitors (henceforth “MDM2i”). The invention relates tothe analysis that at least one of the biomarkers in Table 2 provides a“gene signature” for MDM2i that has increased accuracy and specificityin predicting which cancer cells are sensitive to MDM2i. The methodanalyzes the gene expression or protein level of at least one of thebiomarkers in Table 2 in a cancer sample taken from a patient andcompared to a baseline control predicts the sensitivity of the cancersample to an MDM2i. The pattern of expression level changes may beindicative of a favorable response or an unfavorable one. In addition,the gene signature provided in Table 2 has increased predictive valuebecause it also indicates that the p53 pathway is functional. This is anunexpected result as many tumors contain a mutated p53 and anon-functional pathway which provides the tumor with a growth advantage.The invention is an example of “personalized medicine” wherein patientsare treated based on a functional genomic signature that is specific tothat individual.

The predictive value of at least one biomarker in Table 2 can also beused after treatment with an MDM2i to determine if the patient remainssensitive to the treatment. Once the MDM2i therapeutic has beenadministered, the biomarkers are used to monitor the continuedsensitivity of the patient to MDM2i treatment. The disclosurealsorelates to the up or down regulation of the expression of theidentified genes after MDM2i treatment. This is useful in determiningthat patients receive the correct course of treatment. The inventioncomprises a method of predicting and monitoring the sensitivity of apatient to MDM2i treatment. The method includes the step ofadministration of an MDM2i to the patient and measurement of biomarkergene expression on a biological sample obtained from the patient. Theresponse of the patient is evaluated based on the detection of geneexpression of at least one biomarker from Table 2. Detection and/oralteration in the level of expression of at least one biomarker comparedto baseline is indicative of the sensitivity of the patient to thetreatment. The pattern of expression level changes can be indicative ofa favorable patient response or an unfavorable one.

DESCRIPTION OF THE DRAWINGS

FIG. 1A shows the in vitro potency of the MDM2i in disrupting p53-MDM2interaction. FIG. 1B shows the in vitro potency of the MDM2i on theproliferation of cancer cells.

FIG. 2 shows the p53 status (mutant or wild type) and the sensitivity ofthe cancer cells to MDM2i(2). The X axis is the crossing point forsensitivity and the Y axis is the Amax value.

FIG. 3 are biomarkers showing the fold change in expression and thestatistical significance of the overexpression value.

FIG. 4 is a Western blot of sensitive and insensitive representativecells treated with an MDM2i(1) for 4 hours at various concentrations andthen probed for selected p53 target genes as pharmacodynamic biomarkerrepresentatives.

FIG. 5 is a graph demonstrating the increase in predictive value of thegene signature as opposed to a larger set of biomarkers.

FIG. 6 is a list of cell lines, each analyzed for the gene signature andthe prediction of whether the cell line is sensitive or insensitive andthe IC50 when treated with an MDM2i.

FIG. 7A-C shows the dose-dependent inhibition of tumor growth in theSJSA-1 xenograft model (predicted to be sensitive by the gene signature)following treatment with MDM2i(1), and the concomitant induction ofp21(CDKN1A) expression as a representative pharmacodynamic biomarker.

FIG. 8 shows the expression of p21(CDKN1A) at the protein level aftertreatment of MDM2i(1) sensitive cells at efficacious doses.

FIG. 9 is a model of tumor samples predicted to be sensitive using thegene signature in the OncExpress database and the Primary Tumor Bank.

FIG. 10 represents the Positive Predictive Values (PPV) achieved by theMDM2i(2) sensitivity predictive models, built from multiple combinationsof biomarkers described in Table 2.

FIG. 11 represents the Specificities achieved by the MDM2i sensitivitypredictive models, built from multiple combinations of biomarkersdescribed in Table 2.

FIG. 12 represents the Sensitivities achieved by the MDM2i sensitivitypredictive models, built from multiple combinations of biomarkersdescribed in Table 2.

FIG. 13 represents the PPV achieved by the MDM2i sensitivity predictivemodels, built from multiple combinations of biomarkers described inTable 2, together with p53 mutation status.

FIG. 14 represents the Specificities achieved by the MDM2i sensitivitypredictive models, built from multiple combinations of biomarkersdescribed in Table 2, together with p53 mutation status.

FIG. 15 represents the Sensitivities achieved by the MDM2i sensitivitypredictive models, built from multiple combinations of biomarkersdescribed in Table 2, together with p53 mutation status.

DESCRIPTION OF THE INVENTION

The aspects, features and embodiments of the present invention aresummarized in the following items and can be used respectively alone orin combination:

1. A method of predicting the sensitivity of a cancer patient fortreatment with a Human Double Minute 2 inhibitor (MDM2i), the methodcomprising: a) measuring differential gene expression of at least onebiomarker selected from Table 2 in a cancer sample obtained from thepatient; and b) comparing the differential gene expression of the atleast one biomarker with gene expression of said biomarker in a controlsample, wherein the increase or decrease in gene expression comparisonindicates that the patient is sensitive to treatment with an MDM2i.

2. A method of treating a cancer patient comprising: a) measuringdifferential gene expression of at least one biomarker selected fromTable 2 in a cancer sample obtained from the patient; b) comparing thedifferential gene expression of the at least one biomarker with geneexpression of the biomarker in a control sample; c) determiningsensitivity of the patient to an MDM2i; and d) administering to thepatient an MDM2i.

3. A method of predicting the sensitivity of a cancer cell to a HumanDouble Minute 2 inhibitor (MDM2i), the method comprising: a) measuringdifferential gene expression of at least one biomarker selected fromTable 2 in the cell; b) comparing the differential gene expression ofthe at least one biomarker selected from Table 2 with gene expressionfrom a normal or control cell.

4. A method of determining the sensitivity of a cancer cell to a HumanDouble Minute 2 inhibitor (MDM2i), the method comprising: a) contactinga cancer cell with at least one MDM2i; b) measuring differential geneexpression of at least one biomarker selected from Table 2 in the cellcontacted with the MDM2i; c) comparing the differential gene expressionof the at least one biomarker with gene expression of the biomarker froman untreated or placebo treated control cell; d) wherein there is anincrease in the expression of at the least one biomarker when comparedwith the expression of the at least one biomarker from the untreated orplacebo treated control cell.

5. The method of any one of items 1 to 3, wherein more than onebiomarker is selected from Table 2.

6. The method of item 1 or 4, wherein at least two, at least three, atleast four, at least five, at least six, at least seven, at least eight,at least nine, at least ten, at least eleven, at least twelve or allthirteen biomarkers are selected from Table 2.

7. The method of any one of items 1 to 5, wherein p53 is selected as abiomarker in addition to any biomarker selected from Table 2.

8. The method of any one of items 1 to 6, comprising the biomarkersMDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,TNFRSF10B and/or AEN.

9. The method of any one of items 1 to 7, wherein comparing thedifferential gene expression of the at least one biomarker with geneexpression of a control sample indicates a functional p53 gene pathway.

10. The method of any one of items 1 to 8, wherein the cancer sample isselected from the group consisting of breast, lung, pancreas, ovary,central nervous system (CNS), endometrium, stomach, large intestine,colon, esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,skin, melanoma, kidney, soft tissue sarcoma and pleura.

11. The method of any one of items 1 to 9, wherein a nucleic acid orprotein of at least one biomarker is measured.

12. The method of any one of items 1, 2 or 5 to 11, wherein theexpression of the at least one biomarker is increased in the cancersample when compared to a control sample.

13. The method of any one of items 1 to 11, wherein the MDM2i isselected from Table 1.

14. The method of any one of items 1 to 12, wherein the MDM2i is acompound that binds to a p53 binding pocket of MDM2.

15. The method of any one of items 1 to 13, wherein the MDM2i is acompound that binds to substantially the same p53 binding pocket of MDM2as Nutlin-3a or the MDM2i from the Table 1.

16. The method of any one of items 1 to 14, wherein the MDM2i is acompound that prevents the protein-protein interaction between p53 andMDM2.

17. The method of any one of items 1 to 15, wherein the MDM2i is acompound that inhibits cell proliferation by inducing the p53 pathwayactivity.

18. The method of any one of items 2 to 16 further comprising obtaininga biological sample from the patient prior to the administration of theMDM2i.

19. The method of any one of items 2 to 17, wherein the MDM2i isadministered in a therapeutically effective amount.

20. The method of any one of items 3 to 11, or 13 to 19, wherein thegene expression of the at least one biomarker is increased in the cancercell.

21. The method of any one of items 4 to 11, or 13 to 20, wherein theIC50 of the cancer cell contacted with at least one MDM2i is less than 1μM

22. The method of any one of items 4 to 11, or 13 to 21, wherein thecell is contacted by the MDM2i at least at two different time points.

23. The method of any one of items 4 to 11, or 13 to 22, wherein thecell is contacted by two different MDM2i at step a).

24. The method of item 23, wherein the cell is contacted by the twodifferent MDM2i at the same time.

25. The method of item 23, wherein the cell is contacted by twodifferent MDM2i at different time points.

26. The method of any one of items 4 to 11, or 13 to 25, wherein thesteps b) and c) are repeated at a time points selected from the groupconsisting of: 4 hours, 8 hours, 16 hours, 24 hours, 48 hours, 3 days, 1week, 1 month and several months after administration of each dose ofMDM2i.

27. A method of screening for MDM2i candidates the method comprising: a)contacting a cell with a MDM2i candidate; b) measuring gene expressionof at least one biomarker selected from Table 2 in the cell contactedwith the MDM2i candidate; and c) comparing the gene expression of the atleast one biomarker selected from Table 2 from the cell contacted withthe MDM2i candidate with gene expression of the at least one biomarkerselected from Table 2 from a cell contacted with an MDM2i taken fromTable 1 and the gene expression of at least one biomarker of anuntreated or placebo treated cell.

28. The method of item 27, wherein the differential gene expression ofthe MDM2i candidate is compared with the differential gene expression ofan MDM2i selected from Table 1.

29. The method of item 27 or 28, wherein the MDM2i candidate increasesgene expression of at least one, at least two, at least three, at leastfour, at least five, at least six, at least seven, at least eight, atleast nine, at least ten, at least eleven, at least twelve or allthirteen biomarkers from Table 2.

30. The method of any one of items 27 to 29, wherein the cell is acancer cell selected from the group consisting of breast, lung,pancreas, ovary, central nervous system (CNS), endometrium, stomach,large intestine, colon, esophagus, bone, urinary tract, hematopoietic,lymphoid, liver, skin, melanoma, kidney, soft tissue sarcoma and pleura.

31. The method of any one of items 27 to 30, wherein the expression ofnucleic acid or protein of at least one biomarker of Table 2 ismeasured.

32. The method of any one of items 27 to 31, comprising the biomarkersMDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,TNFRSF10B and AEN.

33. Composition comprising an MDM2i for use in treatment of cancer in aselected cancer patient population, wherein the cancer patientpopulation is selected on the basis of showing an increased geneexpression rate of at least one biomarker selected from Table 2 in acancer cell sample obtained from said patients compared to a normalcontrol cell sample.

34. The composition of item 33, wherein at least two, at least three, atleast four, at least five, at least six, at least seven, at least eight,at least nine, at least ten, at least eleven, at least twelve or allthirteen biomarkers are selected from Table 2.

35. The composition of items 33 or 34, wherein p53 is selected as abiomarker in addition to any biomarker selected from Table 2.

36. The composition of any one of items 33 to 35, wherein the biomarkeris MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1,XPC, TNFRSF10B and/or AEN.

37. The composition of any one of items 33 to 36, wherein the MDM2i isselected from Table 1.

38. The composition of any one of items 33 to 37, wherein the MDM2i is acompound that binds to a p53 binding pocket of MDM2.

39. The composition of any one of items 33 to 38, wherein the MDM2i is acompound that binds to substantially the same p53 binding pocket of MDM2as Nutlin-3a or the MDM2i from the Table 1.

40. The composition of any one of items 33 to 39, wherein the MDM2i is acompound that prevents the protein-protein interaction between p53 andMDM2.

41. The composition of any one of items 33 to 40, wherein the MDM2i is acompound that inhibits cell proliferation by inducing the p53 pathwayactivity.

42. The composition of any one of items 33 to 41, wherein the cancercell sample is selected from the group consisting of breast, lung,pancreas, ovary, central nervous system (CNS), endometrium, stomach,large intestine, colon, esophagus, bone, urinary tract, hematopoietic,lymphoid, liver, skin, melanoma, kidney, soft tissue sarcoma and pleura.

43. The composition of any one of items 33 to 42, wherein the patientsare selected on the basis of an increased gene expression of thebiomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1,CCNG1, XPC, TNFRSF10B and AEN.

44. A kit for predicting the sensitivity of a cancer patient fortreatment with a Human Double Minute 2 inhibitor (MDM2i) comprising: i)means for detecting the expression of any one of the biomarkers from thetable 2, preferably more than one, particularly at least two, at leastthree, at least four, at least five, at least six, at least seven, atleast eight, at least nine, at least ten, at least eleven, at leasttwelve or all thirteen biomarkers selected from Table 2; and ii)instructions how to use said kit.

45. The kit of item 44, wherein the biomarkers are MDM2, CDKN1A, ZMAT3,DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B or/and AEN.

46. The kit of item 45 further comprising means for detecting theexpression of p53.

47. Use of the kit according to item 45 or 46 for any of the methods ofitems 1 to 32.

Further aspects describe the invention:

In one aspect, a disclosed invention relates to methods of analyzing atleast one of the biomarkers identified in Table 2 in a sample containingcancer cells wherein increased or decreased expression of at least onebiomarker when compared to a baseline indicates if the cancer cell willbe sensitive to MDM2i treatment. The pattern of expression level changescan be indicative of a favorable patient response or of an unfavorableone and patients can be selected or rejected based on the increased ordecreased expression of at least one biomarker from Table 2.Alternatively, all of the biomarkers in Table 2 can be assayed for as asingle set.

After treatment with an MDM2i, the invention relates to methods ofanalyzing at least one of the biomarkers identified in Table 2 in asample containing cancer cells wherein increased or decreased expressionof the biomarker when compared to a baseline control after MDM2itreatment indicates that the patient is still sensitive to MDM2itreatment. Detection and/or alteration in the level of expression of atleast one biomarker compared to a baseline is indicative of the MDM2isensitivity, and this correlates with a response of the patient to thetreatment. Alternatively, all of the biomarkers in Table 2 can beassayed for as a single set. The pattern of expression level changes canbe indicative of a favorable patient response or of an unfavorable one.

Accordingly, the invention provides for a method of predicting thesensitivity of a cancer patient for treatment with a Human Double Minute2 inhibitor (MDM2i), the method comprising: a) measuring differentialgene expression of at least one biomarker selected from Table 2 in acancer sample obtained from the patient; and b) comparing thedifferential gene expression of the at least one biomarker with geneexpression of a control sample, wherein the increase or decrease in geneexpression comparison indicates that the patient is sensitive totreatment with an MDM2i.

The method wherein more than one biomarker is selected from Table 2.

The method comprising the biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR,RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and AEN.

The method wherein comparing the differential gene expression of the atleast one biomarker with gene expression of a control sample indicates afunctional p53 gene pathway.

The method wherein the cancer sample is selected from the groupconsisting of: breast, lung, pancreas, ovary, central nervous system(CNS), endometrium, stomach, large intestine, colon, esophagus, bone,urinary tract, hematopoietic, lymphoid, liver, skin, melanoma, kidney,soft tissue sarcoma and pleura.

The method wherein a nucleic acid or protein of at least one biomarkeris measured.

The method wherein the gene expression of the at least one biomarker isincreased.

The method wherein the MDM2i is selected from Table 1.

The method wherein the MDM2i is administered in a therapeuticallyeffective amount.

A method of treating a cancer patient comprising: a) measuringdifferential gene expression of at least one biomarker selected fromTable 2 in a cancer sample obtained from the patient; b) comparing thedifferential gene expression of the at least one biomarker with geneexpression of a control sample; c) determining sensitivity of thepatient to an MDM2i; and d) administering to the patient an MDM2i.

The method wherein more than one biomarker is selected from Table 2.

The method of comprising the biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR,RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and AEN.

The method wherein the at least one biomarker indicates a functional p53gene pathway.

The method further comprising obtaining a biological sample from thepatient prior to the administration of the MDM2i.

The method wherein the cancer sample is selected from the groupconsisting of: breast, lung, pancreas, ovary, central nervous system(CNS), endometrium, stomach, large intestine, colon, esophagus, bone,urinary tract, hematopoietic, lymphoid, liver, skin, melanoma, kidney,soft tissue sarcoma and pleura.

The method wherein the MDM2i is selected from Table 1.

The method wherein the MDM2i is administered in a therapeuticallyeffective amount.

A method of predicting the sensitivity of a cancer cell to a HumanDouble Minute 2 inhibitor (MDM2i), the method comprising: a) measuringdifferential gene expression of at least one biomarker selected fromTable 2 in the cell b) comparing the differential gene expression of theat least on biomarker selected from Table 2 with gene expression from anormal or control cell.

The method wherein the MDM2i is selected from Table 1.

The method wherein more than one biomarker is selected from Table 2.

The method comprising the biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR,RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and AEN.

The method wherein comparing the differential gene expression of the atleast one biomarker with gene expression of a control sample indicates afunctional p53 gene pathway.

The method wherein the cancer sample is selected from the groupconsisting of: breast, lung, pancreas, ovary, central nervous system(CNS), endometrium, stomach, large intestine, colon, esophagus, bone,urinary tract, hematopoietic, lymphoid, liver, skin, melanoma, kidney,soft tissue sarcoma and pleura.

The method wherein a nucleic acid or protein of at least one biomarkeris measured.

The method wherein the gene expression of the at least one biomarker isincreased.

The method wherein the MDM2i is selected from Table 1.

The method wherein the MDM2i is administered in a therapeuticallyeffective amount.

A method of assaying for the sensitivity of a cancer cell to a HumanDouble Minute 2 inhibitor (MDM2i), the method comprising: a) contactinga cancer cell with at least one MDM2i; b) measuring differential geneexpression of at least one biomarker selected from Table 2 in the cancercell contacted with the MDM2i; c) comparing the differential geneexpression with gene expression from an untreated or placebo treatedcontrol cell; d) wherein the IC50 of the cancer cell contacted with atleast one MDM2i is less than 3 μM.

The method wherein the cancer cell is contacted by the MDM2i at leasttwo different time points.

The method wherein the cancer cell is contacted by two different MDM2iat step a).

The method wherein the cancer cell is contacted by the two differentMDM2i at the same time.

The method wherein the cancer cell is contacted by two different MDM2iat different time points.

The method wherein the cancer cell is selected from the group consistingof breast, lung, pancreas, ovary, central nervous system (CNS),endometrium, stomach, large intestine, colon, esophagus, bone, urinarytract, hematopoietic, lymphoid, liver, skin, melanoma, kidney, softtissue sarcoma and pleura.

The method wherein a nucleic acid or protein of at least one biomarkeris measured.

The method wherein the gene expression of the at least one biomarker isincreased.

The method comprising the biomarkers: MDM2, CDKN1A, ZMAT3, DDB2, FDXR,RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and AEN.

The method wherein the steps b) and c) are repeated at time points of: 4hours, 8 hours, 16 hours, 24 hours, 48 hours, 3 days, 1 week, 1 monthand 2 months after contact with an MDM2i.

A method of screening for MDM2i candidates the method comprising: a)contacting a cell with a MDM2i candidate; b) measuring differential geneexpression of at least one biomarker selected from Table 2 in the cellcontacted with the MDM2i candidate; and c) comparing the differentialgene expression of at least one biomarker selected from Table 2 from thecell contacted with the MDM2i candidate with differential geneexpression of at least one biomarker selected from Table 2 from a cellcontacted with an MDM2i taken from Table 1 and the differential geneexpression of at least one biomarker of an untreated or placebo treatedcell.

The method wherein the differential gene expression of the MDM2icandidate is compared with the differential gene expression of an MDM2iselected from Table 1. The method wherein the MDM2i candidate increasesgene expression of at least one biomarker of Table 2.

The method wherein the cancer cell is selected from the group consistingof breast, lung, pancreas, ovary, central nervous system (CNS),endometrium, stomach, large intestine, colon, esophagus, bone, urinarytract, hematopoietic, lymphoid, liver, skin, melanoma, kidney, softtissue sarcoma and pleura.

The method wherein the expression of nucleic acid or protein of at leastone biomarker of Table 2 is measured.

The method comprising the biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR,RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and AEN.

Composition comprising an MDM2i for use in treatment of cancer in aselected cancer patient population, wherein the cancer patientpopulation is selected on the basis of showing an increased geneexpression rate of at least one biomarker selected from Table 2 in acancer cell sample obtained from said patients compared to a normalcontrol cell sample. The composition wherein the cancer sample isselected from the group consisting of breast, lung, pancreas, ovary,central nervous system (CNS), endometrium, stomach, large intestine,colon, esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,skin, melanoma, kidney, soft tissue sarcoma and pleura.

The composition wherein the patients are selected on the basis of anincreased gene expression of the biomarkers MDM2, CDKN1A, ZMAT3, DDB2,FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and AEN.

A kit for predicting the sensitivity of a cancer patient for treatmentwith a Human Double Minute 2 inhibitor (MDM2i) comprising: i) means fordetecting the expression of the biomarkers MDM2, CDKN1A, ZMAT3, DDB2,FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and AEN; and ii)instructions how to use said kit.

DEFINITIONS

As used in the specification and claims, the singular form “a”, “an” and“the” include plural references unless the context clearly dictatesotherwise. For example, the term “a cell” includes a plurality of cells,including mixtures thereof.

All numerical designations, e.g., pH, temperature, time, concentration,and molecular weight, including ranges, are approximations which arevaried (+) or (−) by increments of 0.1. It is to be understood, althoughnot always explicitly stated that all numerical designations arepreceded by the term “about.” It also is to be understood, although notalways explicitly stated, that the reagents described herein are merelyexemplary and that equivalents of such are known in the art.

The terms “marker” or “biomarker” are used interchangeably herein. Abiomarker is a nucleic acid or polypeptide and the presence, absence ordifferential expression of the nucleic acid or polypeptide is used todetermine sensitivity to any MDM2i. For example, CDKN1A is a biomarkerand the mRNA expression of CDKN1A in a cancer cell is increased whencompared to CDKN1A expression in normal (non-cancerous) tissue orcontrol tissue.

“MDM2” refers to an E3 ubiquitin-protein ligase that mediates theubiquitination of p53, permits the nuclear export of p53 and triggersp53 degradation. Unless specifically stated otherwise, MDM2 as usedherein, refers to human MDM2-accession numbers NM_002392/NP_002383 (SEQID NO. 1/SEQ ID NO. 2).

A cell is “sensitive” or displays “sensitivity” for inhibition with anMDM2i when at least one of the biomarkers disclosed in Table 2 isdifferentially expressed. Alternatively, a cell is “sensitive” forinhibition with an MDM2i when all of the biomarkers disclosed in Table 2as a set are differentially expressed.

A “control cell” or “normal cell” refers to non-cancerous tissue orcell.

A “control tissue” or “normal tissue” refers to non-cancerous tissue orcell.

A “control sample” or “normal sample” refers to non-cancerous tissue orcell.

The terms “nucleic acid” and “polynucleotide” are used interchangeablyand refer to a polymeric form of nucleotides of any length, eitherdeoxyribonucleotides or ribonucleotides or analogs thereof.Polynucleotides can have any three-dimensional structure and may performany function. The following are non-limiting examples ofpolynucleotides: a gene or gene fragment (for example, a probe, primer,EST or SAGE tag), exons, introns, messenger RNA (mRNA), transfer RNA,ribosomal RNA, ribozymes, cDNA, recombinant polynucleotides, branchedpolynucleotides, plasmids, vectors, isolated DNA of any sequence,isolated RNA of any sequence, nucleic acid probes, and primers. Apolynucleotide can comprise modified nucleotides, such as methylatednucleotides and nucleotide analogs. If present, modifications to thenucleotide structure can be imparted before or after assembly of thepolymer. The sequence of nucleotides can be interrupted bynon-nucleotide components. A polynucleotide can be further modifiedafter polymerization, such as by conjugation with a labeling component.The term also refers to both double- and single-stranded molecules.Unless otherwise specified or required, any embodiment of this inventionthat is a polynucleotide encompasses both the double-stranded form andeach of two complementary single-stranded forms known or predicted tomake up the double-stranded form.

A “gene” refers to a polynucleotide containing at least one open readingframe (ORF) that is capable of encoding a particular polypeptide orprotein after being transcribed and translated. A polynucleotidesequence can be used to identify larger fragments or full-length codingsequences of the gene with which they are associated. Methods ofisolating larger fragment sequences are known to those of skill in theart.

“Gene expression” or alternatively a “gene product” refers to thenucleic acids or amino acids (e.g., peptide or polypeptide) generatedwhen a gene is transcribed and translated.

The term “polypeptide” is used interchangeably with the term “protein”and in its broadest sense refers to a compound of two or more subunitamino acids, amino acid analogs, or peptidomimetics. The subunits can belinked by peptide bonds. In another embodiment, the subunit may belinked by other bonds, e.g., ester, ether, etc.

As used herein the term “amino acid” refers to either natural and/orunnatural or synthetic amino acids, and both the D and L opticalisomers, amino acid analogs, and peptidomimetics. A peptide of three ormore amino acids is commonly called an oligopeptide if the peptide chainis short. If the peptide chain is long, the peptide is commonly called apolypeptide or a protein.

The term “isolated” means separated from constituents, cellular andotherwise, in which the polynucleotide, peptide, polypeptide, protein,antibody or fragment(s) thereof, are normally associated with in nature.For example, an isolated polynucleotide is separated from the 3′ and 5′contiguous nucleotides with which it is normally associated within itsnative or natural environment, e.g., on the chromosome. As is apparentto those of skill in the art, a non-naturally occurring polynucleotide,peptide, polypeptide, protein, antibody, or fragment(s) thereof, doesnot require “isolation” to distinguish it from its naturally occurringcounterpart. In addition, a “concentrated,” “separated” or “diluted”polynucleotide, peptide, polypeptide, protein, antibody or fragment(s)thereof, is distinguishable from its naturally occurring counterpart inthat the concentration or number of molecules per volume is greater in a“concentrated” version or less than in a “separated” version than thatof its naturally occurring counterpart. A polynucleotide, peptide,polypeptide, protein, antibody, or fragment(s) thereof, which differsfrom the naturally occurring counterpart in its primary sequence or, forexample, by its glycosylation pattern, need not be present in itsisolated form since it is distinguishable from its naturally occurringcounterpart by its primary sequence or, alternatively, by anothercharacteristic such as glycosylation pattern. Thus, a non-naturallyoccurring polynucleotide is provided as a separate embodiment from theisolated naturally occurring polynucleotide. A protein produced in abacterial cell is provided as a separate embodiment from the naturallyoccurring protein isolated from a eukaryotic cell in which it isproduced in nature.

A “probe” when used in the context of polynucleotide manipulation refersto an oligonucleotide that is provided as a reagent to detect a targetpotentially present in a sample of interest by hybridizing with thetarget. Usually, a probe will comprise a label or a means by which alabel can be attached, either before or subsequent to the hybridizationreaction. Suitable labels include, but are not limited to radioisotopes,fluorochromes, chemiluminescent compounds, dyes, and proteins, includingenzymes.

A “primer” is a short polynucleotide, generally with a free 3′—OH groupthat binds to a target or “template” potentially present in a sample ofinterest by hybridizing with the target, and thereafter promotingpolymerization of a polynucleotide complementary to the target. A“polymerase chain reaction” (“PCR”) is a reaction in which replicatecopies are made of a target polynucleotide using a “pair of primers” ora “set of primers” consisting of an “upstream” and a “downstream”primer, and a catalyst of polymerization, such as a DNA polymerase, andtypically a thermally-stable polymerase enzyme. Methods for PCR are wellknown in the art, and taught, for example in PCR: A Practical Approach,M. MacPherson et al., IRL Press at Oxford University Press (1991). Allprocesses of producing replicate copies of a polynucleotide, such as PCRor gene cloning, are collectively referred to herein as “replication.” Aprimer can also be used as a probe in hybridization reactions, such asSouthern or Northern blot analyses (Sambrook et al., Molecular Cloning:A Laboratory Manual, 2nd edition (1989)).

As used herein, “expression” refers to the process by which DNA istranscribed into mRNA and/or the process by which the transcribed mRNAis subsequently translated into peptides, polypeptides or proteins. Ifthe polynucleotide is derived from genomic DNA, expression may includesplicing of the mRNA in a eukaryotic cell.

“Differentially expressed” as applied to a gene, refers to thedifferential production of the mRNA transcribed and/or translated fromthe gene or the protein product encoded by the gene. A differentiallyexpressed gene may be overexpressed or underexpressed as compared to theexpression level of a normal or control cell. However, as used herein,overexpression is an increase in gene expression and generally is atleast 1.25 fold or, alternatively, at least 1.5 fold or, alternatively,at least 2 fold, or alternatively, at least 3 fold or alternatively, atleast 4 fold expression over that detected in a normal or controlcounterpart cell or tissue. As used herein, underexpression, is areduction of gene expression and generally is at least 1.25 fold, oralternatively, at least 1.5 fold, or alternatively, at least 2 fold oralternatively, at least 3 fold or alternatively, at least 4 foldexpression under that detected in a normal or control counterpart cellor tissue. The term “differentially expressed” also refers to whereexpression in a cancer cell or cancerous tissue is detected butexpression in a control cell or normal tissue (e.g. non-cancerous cellor tissue) is undetectable.

A high expression level of the gene may occur because of over expressionof the gene or an increase in gene copy number. The gene may also betranslated into increased protein levels because of deregulation orabsence of a negative regulator.

A “gene expression profile” refers to a pattern of expression of atleast one biomarker that recurs in multiple samples and reflects aproperty shared by those samples, such as tissue type, response to aparticular treatment, or activation of a particular biological processor pathway in the cells. Furthermore, a gene expression profiledifferentiates between samples that share that common property and thosethat do not with better accuracy than would likely be achieved byassigning the samples to the two groups at random. A gene expressionprofile may be used to predict whether samples of unknown status sharethat common property or not. Some variation between the levels of atleast one biomarker and the typical profile is to be expected, but theoverall similarity of the expression levels to the typical profile issuch that it is statistically unlikely that the similarity would beobserved by chance in samples not sharing the common property that theexpression profile reflects.

The term “cDNA” refers to complementary DNA, i.e. mRNA molecules presentin a cell or organism made into cDNA with an enzyme such as reversetranscriptase. A “cDNA library” is a collection of all of the mRNAmolecules present in a cell or organism, all turned into cDNA moleculeswith the enzyme reverse transcriptase, then inserted into “vectors”(other DNA molecules that can continue to replicate after addition offoreign DNA). Exemplary vectors for libraries include bacteriophage(also known as “phage”), viruses that infect bacteria, for example,lambda phage. The library can then be probed for the specific cDNA (andthus mRNA) of interest.

As used herein, “solid phase support” or “solid support”, usedinterchangeably, is not limited to a specific type of support. Rather alarge number of supports are available and are known to one of ordinaryskill in the art. Solid phase supports include silica gels, resins,derivatized plastic films, glass beads, plastic beads, alumina gels,microarrays, and chips. As used herein, “solid support” also includessynthetic antigen-presenting matrices, cells, and liposomes. A suitablesolid phase support may be selected on the basis of desired end use andsuitability for various protocols. For example, for peptide synthesis,solid phase support may refer to resins such as polystyrene (e.g.,PAM-resin obtained from Bachem Inc., Peninsula Laboratories),polyHIPE(R)™ resin (obtained from Aminotech, Canada), polyamide resin(obtained from Peninsula Laboratories), polystyrene resin grafted withpolyethylene glycol (TentaGelR™, Rapp Polymere, Tubingen, Germany), orpolydimethylacrylamide resin (obtained from Milligen/Biosearch,California).

A polynucleotide also can be attached to a solid support for use in highthroughput screening assays. PCT WO 97/10365, for example, discloses theconstruction of high density oligonucleotide chips. See also, U.S. Pat.Nos. 5,405,783; 5,412,087 and 5,445,934. Using this method, the probesare synthesized on a derivatized glass surface to form chip arrays.Photoprotected nucleoside phosphoramidites are coupled to the glasssurface, selectively deprotected by photolysis through aphotolithographic mask and reacted with a second protected nucleosidephosphoramidite. The coupling/deprotection process is repeated until thedesired probe is complete.

As an example, transcriptional activity can be assessed by measuringlevels of messenger RNA using a gene chip such as the Affymetrix®HG-U133-Plus-2 GeneChips. High-throughput, real-time quantitation of RNAof a large number of genes of interest thus becomes possible in areproducible system.

The terms “stringent hybridization conditions” refers to conditionsunder which a nucleic acid probe will specifically hybridize to itstarget subsequence, and to no other sequences. The conditionsdetermining the stringency of hybridization include: temperature, ionicstrength, and the concentration of denaturing agents such as formamide.Varying one of these factors may influence another factor and one ofskill in the art will appreciate changes in the conditions to maintainthe desired level of stringency. An example of a highly stringenthybridization is: 0.015M sodium chloride, 0.0015M sodium citrate at65-68° C. or 0.015M sodium chloride, 0.0015M sodium citrate, and 50%formamide at 42° C. (see Sambrook, supra). An example of a “moderatelystringent” hybridization is the conditions of: 0.015M sodium chloride,0.0015M sodium citrate at 50-65° C. or 0.015M sodium chloride, 0.0015Msodium citrate, and 20% formamide at 37-50° C. The moderately stringentconditions are used when a moderate amount of nucleic acid mismatch isdesired. One of skill in the art will appreciate that washing is part ofthe hybridization conditions. For example, washing conditions caninclude 02.X−0.1 X SSC/0.1% SDS and temperatures from 42-68° C., whereinincreasing temperature increases the stringency of the wash conditions.

When hybridization occurs in an antiparallel configuration between twosingle-stranded polynucleotides, the reaction is called “annealing” andthose polynucleotides are described as “complementary.” Adouble-stranded polynucleotide can be “complementary” or “homologous” toanother polynucleotide, if hybridization can occur between one of thestrands of the first polynucleotide and the second. “Complementarity” or“homology” (the degree that one polynucleotide is complementary withanother) is quantifiable in terms of the proportion of bases in opposingstrands that are expected to form hydrogen bonding with each other,according to generally accepted base-pairing rules.

A polynucleotide or polynucleotide region (or a polypeptide orpolypeptide region) has a certain percentage (for example, 80%, 85%,90%, 95%, 98% or 99%) of “sequence identity” to another sequence meansthat, when aligned, that percentage of bases (or amino acids) are thesame in comparing the two sequences. This alignment and the percenthomology or sequence identity can be determined using software programsknown in the art, for example those described in Current Protocols inMolecular Biology, Ausubel et al., eds., (1987) Supplement 30, section7.7.18, Table 7.7.1. Preferably, default parameters are used foralignment. A preferred alignment program is BLAST, using defaultparameters. In particular, preferred programs are BLASTN and BLASTP,using the following default parameters: Genetic code=standard;filter=none; strand=both; cutoff=60; expect=10; Matrix=BLOSUM62;Descriptions=50 sequences; sort by=HIGH SCORE; Databases=non-redundant.

The term “cell proliferative disorders” shall include dysregulation ofnormal physiological function characterized by abnormal cell growthand/or division or loss of function. Examples of “cell proliferativedisorders” include but are not limited to hyperplasia, neoplasia,metaplasia, and various autoimmune disorders, e.g., those characterizedby the dysregulation of T cell apoptosis.

As used herein, the terms “neoplastic cells,” “neoplastic disease,”“neoplasia,” “tumor,” “tumor cells,” “cancer,” and “cancer cells,” (usedinterchangeably) refer to cells which exhibit relatively autonomousgrowth, so that they exhibit an aberrant growth phenotype characterizedby a significant loss of control of cell proliferation (i.e.,de-regulated cell division). Neoplastic cells can be malignant orbenign. A metastatic cell or tissue means that the cell can invade anddestroy neighboring body structures.

The term “cancer” refers to cancer diseases including, for example,breast, lung, pancreas, ovary, central nervous system (CNS),endometrium, stomach, large intestine, colon, esophagus, bone, urinarytract, hematopoietic, lymphoid, liver, skin, melanoma, kidney, softtissue sarcoma and pleura.

The term “PBMC” refers to peripheral blood mononuclear cells andincludes “PBL”—peripheral blood lymphocytes.

“Suppressing” tumor growth indicates a reduction in tumor cell growthwhen contacted with an MDM2i compared to tumor growth without contactwith an MDM2i compound. Tumor cell growth can be assessed by any meansknown in the art, including, but not limited to, measuring tumor size,determining whether tumor cells are proliferating using a 3H-thymidineincorporation assay, measuring glucose uptake by FDG-PET(fluorodeoxyglucose positron emission tomography) imaging, or countingtumor cells. “Suppressing” tumor cell growth means any or all of thefollowing states: slowing, delaying and stopping tumor growth, as wellas tumor shrinkage.

A “composition” is a combination of active agent and another carrier,e.g., compound or composition, inert (for example, a detectable agent orlabel) or active, such as an adjuvant, diluent, binder, stabilizer,buffers, salts, lipophilic solvents, preservative, adjuvant or the like.Carriers also include pharmaceutical excipients and additives, forexample; proteins, peptides, amino acids, lipids, and carbohydrates(e.g., sugars, including monosaccharides and oligosaccharides;derivatized sugars such as alditols, aldonic acids, esterified sugarsand the like; and polysaccharides or sugar polymers), which can bepresent singly or in combination, comprising alone or in combination1-99.99% by weight or volume. Carbohydrate excipients include, forexample; monosaccharides such as fructose, maltose, galactose, glucose,D-mannose, sorbose, and the like; disaccharides, such as lactose,sucrose, trehalose, cellobiose, and the like; polysaccharides, such asraffinose, melezitose, maltodextrins, dextrans, starches, and the like;and alditols, such as mannitol, xylitol, maltitol, lactitol, xylitolsorbitol (glucitol) and myoinositol.

Exemplary protein excipients include serum albumin such as human serumalbumin (HSA), recombinant human albumin (rHA), gelatin, casein, and thelike. Representative amino acid/antibody components, which can alsofunction in a buffering capacity, include alanine, glycine, arginine,betaine, histidine, glutamic acid, aspartic acid, cysteine, lysine,leucine, isoleucine, valine, methionine, phenylalanine, aspartame, andthe like.

The term “carrier” further includes a buffer or a pH adjusting agent;typically, the buffer is a salt prepared from an organic acid or base.Representative buffers include organic acid salts such as salts ofcitric acid, ascorbic acid, gluconic acid, carbonic acid, tartaric acid,succinic acid, acetic acid, or phthalic acid; Tris, tromethaminehydrochloride, or phosphate buffers. Additional carriers includepolymeric excipients/additives such as polyvinylpyrrolidones, ficolls (apolymeric sugar), dextrates (e.g., cyclodextrins, such as2-hydroxypropyl-quadrature-cyclodextrin), polyethylene glycols,flavoring agents, antimicrobial agents, sweeteners, antioxidants,antistatic agents, surfactants (e.g., polysorbates such as TWEEN 20™ andTWEEN 80™), lipids (e.g., phospholipids, fatty acids), steroids (e.g.,cholesterol), and chelating agents (e.g., EDTA).

As used herein, the term “pharmaceutically acceptable carrier”encompasses any of the standard pharmaceutical carriers, such as aphosphate buffered saline solution, water, and emulsions, such as anoil/water or water/oil emulsion, and various types of wetting agents.The compositions also can include stabilizers and preservatives and anyof the above noted carriers with the additional provisio that they beacceptable for use in vivo. For examples of carriers, stabilizers andadjuvants, see Remington's Pharmaceutical Science., 15th Ed. (Mack Publ.Co., Easton (1975) and in the Physician's Desk Reference, 52nd ed.,Medical Economics, Montvale, N.J. (1998).

An “effective amount” is an amount sufficient to effect beneficial ordesired results. An effective amount can be administered in one or moreadministrations, applications or dosages.

A “subject,” “individual” or “patient” is used interchangeably herein,which refers to a vertebrate, preferably a mammal, more preferably ahuman. Mammals include, but are not limited to, mice, simians, humans,farm animals, sport animals, and pets.

An “inhibitor” of MDM2 as used herein reduces the association of p53 andMDM2. This inhibition may include, for example, reducing the associationof p53 and MDM2 before they are bound together, or reducing theassociation of p53 and MDM2 after they are bound together, thus freeingboth molecules.

A number of genes have now been identified as biomarkers for MDM2i. Thedecrease or increase of gene expression of one or more of the biomarkersidentified herein and in Table 2 can be used to determine patientsensitivity to any MDM2i, for example, the increase or overexpression ofa biomarker indicates that a cancer patient is sensitive to and wouldfavorably respond to administration of an MDM2i. As another example,after treatment with a MDM2i, a patient sample can be obtained and thesample assayed for sensitivity to discover if the patient is stillsensitive to the MDM2i treatment. Alternatively, all of the biomarkersin Table 2 can be assayed for as a single set.

MDM2 inhibitors (MDM2i) are compounds which are inhibitors of thep53-MDM2 association, and are useful in conjunction with the methods oruses of the invention. MDM2i are useful in pharmaceutical compositionsfor human or veterinary use where inhibition of the p53-MDM2 associationis indicated, e.g., in the treatment of tumors and/or cancerous cellgrowth. In particular, such compounds are useful in the treatment ofhuman cancer, since the progression of these cancers may be at leastpartially dependent upon overriding the “gatekeeper” function of p53,for example the overexpression of MDM2. MDM2i compounds are useful intreating, for example, carcinomas (e.g., breast, lung, pancreas, ovary,central nervous system (CNS), endometrium, stomach, large intestine,colon, esophagus, bone, urinary tract, hematopoietic, lymphoid, liver,skin, melanoma, kidney, soft tissue sarcoma and pleura A listing ofexemplary MDM2i compounds is found in Table 1 (see WO 2011076786). OtherMDM2i that bind to a p53 binding pocket of MDM2, particularly tosubstantially the same p53 binding pocket of MDM2 as Nutlin-3a orsubstantially where the exemplary MDM2i from the Table 1 binds, can alsobe applied in the methods or uses of the invention. MDM2i used accordingto present embodiments can be structurally related to the one describedin Table 1 (i.e. MDM2i(1) and MDM2i(2)) or to Nutlin 3a, such as, forexample, substituted isoquinolinones, or quinazolinones. The methodsincluded herein can also be used with other compounds such as thespiro-oxindoles, imidazolyl indole and cis-imidazoline (see Shangary etal., Mol. Cancer Ther. 2008 7(6): 1533-1542: Furet et al., BioOrg. Med.Chem. Let. 2012 22:3498-3502 and Carol et al., Pediatr. Blood Cancer2012 pages 1-9, published online Jul. 2, 2012, prior to inclusion intojournal). MDM2i as used herein prevents the protein-protein interactionbetween p53 and MDM2 or inhibits cell proliferation by inducing the p53pathway activity.

TABLE 1 MDM2i compounds

MDM2i(1)

MDM2i(2)

Measurement of Gene Expression

Detection of gene expression can be by any appropriate method, includingfor example, detecting the quantity of mRNA transcribed from the gene orthe quantity of cDNA produced from the reverse transcription of the mRNAtranscribed from the gene or the quantity of the polypeptide or proteinencoded by the gene. These methods can be performed on a sample bysample basis or modified for high throughput analysis. For example,using Affymetrix™ U133 microarray chips.

In one aspect, gene expression is detected and quantitated byhybridization to a probe that specifically hybridizes to the appropriateprobe for that biomarker. The probes also can be attached to a solidsupport for use in high throughput screening assays using methods knownin the art. WO 97/10365 and U.S. Pat. Nos. 5,405,783, 5,412,087 and5,445,934, for example, disclose the construction of high densityoligonucleotide chips which can contain one or more of the sequencesdisclosed herein. Using the methods disclosed in U.S. Pat. Nos.5,405,783, 5,412,087 and 5,445,934, the probes of this invention aresynthesized on a derivatized glass surface. Photoprotected nucleosidephosphoramidites are coupled to the glass surface, selectivelydeprotected by photolysis through a photolithographic mask, and reactedwith a second protected nucleoside phosphoramidite. Thecoupling/deprotection process is repeated until the desired probe iscomplete.

In one aspect, the expression level of a gene is determined throughexposure of a nucleic acid sample to the probe-modified chip. Extractednucleic acid is labeled, for example, with a fluorescent tag, preferablyduring an amplification step. Hybridization of the labeled sample isperformed at an appropriate stringency level. The degree ofprobe-nucleic acid hybridization is quantitatively measured using adetection device. See U.S. Pat. Nos. 5,578,832 and 5,631,734.

Alternatively any one of gene copy number, transcription, or translationcan be determined using known techniques. For example, an amplificationmethod such as PCR may be useful. General procedures for PCR are taughtin MacPherson et al., PCR: A Practical Approach, (IRL Press at OxfordUniversity Press (1991)). However, PCR conditions used for eachapplication reaction are empirically determined. A number of parametersinfluence the success of a reaction. Among them are annealingtemperature and time, extension time, Mg 2+ and/or ATP concentration,pH, and the relative concentration of primers, templates, anddeoxyribonucleotides. After amplification, the resulting DNA fragmentscan be detected by agarose gel electrophoresis followed by visualizationwith ethidium bromide staining and ultraviolet illumination.

In one embodiment, the hybridized nucleic acids are detected bydetecting one or more labels attached to the sample nucleic acids. Thelabels can be incorporated by any of a number of means well known tothose of skill in the art. However, in one aspect, the label issimultaneously incorporated during the amplification step in thepreparation of the sample nucleic acid. Thus, for example, polymerasechain reaction (PCR) with labeled primers or labeled nucleotides willprovide a labeled amplification product. In a separate embodiment,transcription amplification, as described above, using a labelednucleotide (e.g. fluorescein-labeled UTP and/or CTP) incorporates alabel in to the transcribed nucleic acids.

Alternatively, a label may be added directly to the original nucleicacid sample (e.g., mRNA, polyA, mRNA, cDNA, etc.) or to theamplification product after the amplification is completed. Means ofattaching labels to nucleic acids are well known to those of skill inthe art and include, for example nick translation or end-labeling (e.g.with a labeled RNA) by kinasing of the nucleic acid and subsequentattachment (ligation) of a nucleic acid linker joining the samplenucleic acid to a label (e.g., a fluorophore).

Detectable labels suitable for use in the present invention include anycomposition detectable by spectroscopic, photochemical, biochemical,immunochemical, electrical, optical or chemical means. Useful labels inthe present invention include biotin for staining with labeledstreptavidin conjugate, magnetic beads (e.g., Dynabeads™), fluorescentdyes (e.g., fluorescein, texas red, rhodamine, green fluorescentprotein, and the like), radiolabels (e.g., 3H, 125I, 35S, 14C, or 32P)enzymes (e.g., horse radish peroxidase, alkaline phosphatase and otherscommonly used in an ELISA), and calorimetric labels such as colloidalgold or colored glass or plastic (e.g., polystyrene, polypropylene,latex, etc.) beads. Patents teaching the use of such labels include U.S.Pat. Nos. 3,817,837; 3,850,752; 3,939,350; 3,996,345; 4,277,437;4,275,149; and 4,366,241.

Detection of labels is well known to those of skill in the art. Thus,for example, radiolabels may be detected using photographic film orscintillation counters, fluorescent markers may be detected using aphotodetector to detect emitted light. Enzymatic labels are typicallydetected by providing the enzyme with a substrate and detecting thereaction product produced by the action of the enzyme on the substrate,and calorimetric labels are detected by simply visualizing the coloredlabel.

The detectable label may be added to the target (sample) nucleic acid(s)prior to, or after the hybridization, such as described in WO 97/10365.These detectable labels are directly attached to or incorporated intothe target (sample) nucleic acid prior to hybridization. In contrast,“indirect labels” are joined to the hybrid duplex after hybridization.Generally, the indirect label is attached to a binding moiety that hasbeen attached to the target nucleic acid prior to the hybridization. Forexample, the target nucleic acid may be biotinylated before thehybridization. After hybridization, an avidin-conjugated fluorophorewill bind the biotin bearing hybrid duplexes providing a label that iseasily detected. For a detailed review of methods of labeling nucleicacids and detecting labeled hybridized nucleic acids see LaboratoryTechniques in Biochemistry and Molecular Biology, Vol. 24: Hybridizationwith Nucleic Acid Probes, P. Tijssen, ed. Elsevier, N.Y. (1993).

Detection of Polypeptides

Expression level of the biomarker can also be determined by examiningprotein expression or the protein product at least one of the biomarkerslisted in Table 2. Determining the protein level involves measuring theamount of any immunospecific binding that occurs between an antibodythat selectively recognizes and binds to the polypeptide of thebiomarker in a sample obtained from a patient and comparing this to theamount of immunospecific binding of at least one biomarker in a controlsample. The amount of protein expression of the biomarker can beincreased or reduced when compared with control expression.Alternatively, all of the biomarkers in Table 2 can be assayed for as asingle set.

A variety of techniques are available in the art for protein analysis.They include but are not limited to radioimmunoassays, ELISA (enzymelinked immunosorbent assays), “sandwich” immunoassays, immunoradiometricassays, in situ immunoassays (using e.g., colloidal gold, enzyme orradioisotope labels), western blot analysis, immunoprecipitation assays,immunofluorescent assays, flow cytometry, immunohistochemistry, confocalmicroscopy, enzymatic assays, surface plasmon resonance and PAGE-SDS.

Assaying for Biomarkers and MDM2i Treatment

Once a patient has been predicted to be sensitive to an MDM2i,administration of any MDM2i to a patient can be effected in one dose,continuously or intermittently throughout the course of treatment.Methods of determining the most effective means and dosage ofadministration are well known to those of skill in the art and will varywith the composition used for therapy, the purpose of the therapy, thetarget cell being treated, and the subject being treated. Single ormultiple administrations can be carried out with the dose level andpattern being selected by the treating physician. Suitable dosageformulations and methods of administering the agents may be empiricallyadjusted.

At least one of the biomarkers provided in Table 2 can be assayed forafter MDM2i administration in order to determine if the patient remainssensitive to the MDM2i treatment. In addition, at least one biomarkercan be assayed for in multiple time points after a single MDM2iadministration. For example, an initial bolus of an MDM2i isadministered, at least one biomarker from Table 2 is assayed for at 1hour, 2 hours, 3 hours, 4 hours, 8 hours, 16 hours, 24 hours, 48 hours,3 days, 1 week or 1 month or several months after the first treatment.Alternatively, all of the biomarkers in Table 2 can be assayed for as asingle set.

The at least one biomarker in Table 2 can be assayed for after eachMDM2i administration, so if there are multiple MDM2i administrations,then at least one biomarker can be assayed for after each administrationto determine continued patient sensitivity. The patient could undergomultiple MDM2i administrations and the biomarkers then assayed atdifferent time points. For example, a course of treatment can requireadministration of an initial dose of MDM2i, a second dose a specifiedtime period later, and still a third dose hours after the second dose.At least one biomarker of Table 2 could be assayed for at 1 hour, 2hours, 3 hours, 4 hours, 8 hours, 16 hours, 24 hours, 48 hours, 3 days,1 week or 1 month or several months after administration of each dose ofMDM2i. Alternatively, all of the biomarkers in Table 2 can be assayedfor as a single set.

It is also within the scope of the invention that different biomarkersare assayed for at different time points. Without being bound to any onetheory, due to mechanism of action of the MDM2i or of the biomarker, theresponse to the MDM2i is delayed and at least one biomarker from Table 2is assayed for at any time after administration to determine if thepatient remains sensitive to MDM2i administration. An assay for at leastone biomarker in Table 2 after each administration of MDM2i will provideguidance as to the means, dosage and course of treatment. Alternatively,all of the biomarkers in Table 2 can be assayed for as a single set.

Finally, there is administration of different MDM2 is and followed byassaying for at least one biomarker in Table 2. In this embodiment, morethan one MDM2i is chosen and administered to the patient. At least onebiomarker from Table 2 can then be assayed for after administration ofeach different MDM2i. This assay can also be done at multiple timepoints after administration of the different MDM2i. For example, a firstMDM2i could be administered to the patient and at least one biomarkerassayed at 1 hour, 2 hours, 3 hours, 4 hours, 8 hours, 16 hours, 24hours, 48 hours, 3 days, 1 week or 1 month or several months afteradministration. A second MDM2i could then be administered and at leastone biomarker could be assayed for again at 1 hour, 2 hours, 3 hours, 4hours, 8 hours, 16 hours, 24 hours, 48 hours, 3 days, 1 week or 1 monthor several months after administration of the second MDM2i. In eachcase, all of the biomarkers in Table 2 can be assayed for as a singleset.

Another aspect of the invention provides for a method of assessing forsuitable dose levels of an MDM2i, comprising monitoring the differentialexpression of at least one of the genes identified in Table 2 afteradministration of the MDM2i. For example, after administration of afirst bolus of MDM2i, at least one biomarker of Table 2 is analyzed andbased on this result, an increase or decrease in MDM2i dosage isrecommended. After administration of the adjusted dosage of MDM2i theanalysis of at least one biomarker will determine whether the patient isstill sensitive to the adjusted dose and that the adjusted dose isproviding the expected benefit, e.g., suppressing tumor growth.Alternatively, all of the biomarkers in Table 2 can be assayed for as asingle set for assessing sensitivity to the dose of the MDM2i.

Kits for assessing the activity of any MDM2i can be made. For example, akit comprising nucleic acid primers for PCR or for microarrayhybridization for the biomarkers listed in Table 2 can be used forassessing MDM2i sensitivity. Alternatively, a kit supplied withantibodies for at least one of the biomarkers listed in Table 2 would beuseful in assaying for MDM2i sensitivity.

It is well known in the art that cancers can become resistant tochemotherapeutic treatment, especially when that treatment is prolonged.Assaying for differential expression of at least one of the biomarkersin Table 2 can be done after prolonged treatment with anychemotherapeutic to determine if the cancer is sensitive to the MDM2i.For example, kinase inhibitors such as Gleevec® will strongly inhibit aspecific kinase, but may also weakly inhibit other kinases. There arealso other MDM2i, for example, the Nutlin family of compounds. If thepatient has been previously treated with another chemotherapeutic oranother MDM2i, it is useful information for the patient to assay for atleast one of the biomarkers in Table 2 to determine if the tumor issensitive to an MDM2i. This assay can be especially beneficial to thepatient if the cancer goes into remission and then re-grows or hasmetastasized to a different site.

Screening for MDM2 Inhibitors

It is possible to assay for at least one biomarker listed in Table 2 toscreen for other MDM2i. This method comprises assaying a cell with atleast one biomarker from Table 2, which predicts if the cell issensitive to an MDM2i candidate inhibitor, the cell is then contactedwith the candidate MDM2i and the 1050 of the treated cell is comparedwith a known MDM2i contacting a sensitive cell. For example, for cellspredicted to be sensitive to any MDM2i as determined by the differentialexpression of at least one biomarker in Table 2, the candidate MDM2iwill have an IC50≦3 μM. The measurement of at least one biomarker fromTable 2 expression can be done by methods described previously, forexample, PCR or microarray analysis. Alternatively, all of thebiomarkers in Table 2 can be assayed for as a single set.

TABLE 2 SEQ ID NO. Gene Name Accession number (nucleotide/protein) MDM2NM_002392/NP_002383 SEQ ID NO. 1/SEQ ID NO. 2 CDKN1A NM_000389/NP_000380SEQ ID NO. 3/SEQ ID NO. 4 ZMAT3 NM_022470/NP_071915 SEQ ID NO. 5/SEQ IDNO. 6 DDB2 NM_000107/NP_000098 SEQ ID NO. 7/SEQ ID NO. 8 FDXRNM_004110/NP_004101 SEQ ID NO. 9/SEQ ID NO. 10 RPS27LNM_015920/NP_057004 SEQ ID NO. 11/SEQ ID NO. 12 BAX NM_004324/NP_004315SEQ ID NO. 13/SEQ ID NO. 14 RRM2B NM_015713/NP_056528 SEQ ID NO. 15/SEQID NO. 16 SESN1 NM_014454/NP_055269 SEQ ID NO. 17/SEQ ID NO. 18 CCNG1NM_004060/NP_004051 SEQ ID NO. 19/SEQ ID NO. 20 XPC NM_004628/NP_004619SEQ ID NO. 21/SEQ ID NO. 22 TNFRSF10B NM_003842/NP_003833 SEQ ID NO.23/SEQ ID NO. 24 AEN NM_022767/NP_073604 SEQ ID NO. 25/SEQ ID NO. 26

EXAMPLES Example 1 Both MDM2i(1) and MDM2i(2) are Equally Potentp53-MDM2 Inhibitors in Biochemical and Cellular Assays

TR-FRET Assay for IC₅₀ determination: standard assay conditionsconsisted of 60 μL total volume in white 384-well plates (GreinerBio-One: Frickenhausen, Germany), in PBS buffer containing 125 mM NaCl,0.001% Novexin, 0.01% Gelatin, 0.2% Pluronic F-127, 1 mM DTT and 1.7%final DMSO). Both MDM2i(1) and MDM2i(2) were added at differentconcentrations to 0.1 nM biotinylated MDM2 (human MDM2 amino acids2-188, internal preparations), 0.1 nM Europium-labeled streptavidin(Perkin Elmer: Waltham, Mass., USA) and 10 nM Cy5-p53 peptide (Cy5-p53aa18-26, internal preparation). After incubation at room temperature for15 minutes, samples were measured on a GeniosPro reader (Tecan:Mannedorf, Germany). FRET assay readout was calculated from the raw dataof the two distinct fluorescence signals measured in time resolved mode(fluorescence 665 nm/fluorescence 620 nm×1000). IC₅₀ values arecalculated by curve fitting using XLfit® (Fit Model #205). This data isshown in FIG. 1A.

Determination of binding rate constants (K_(on), K_(off)): the rapidmixing tool of GeniosPro reader (Tecan: Mannedorf, Germany) was used tostudy fast binding kinetics (single well mode). Microplates containingthe inhibitor and 20 nM Cy5-labeled p53 peptide in 50 μl assay bufferwere placed in the reader. After 10 min equilibration at 25° C., bindingreactions were initiated by injecting 50 μl of buffer containing 0.2 nMbiotinylated MDM2 and 0.2 nM europium-streptavidin at 475 μl/s.Fluorescence was measured at 665 nM and at various time intervals, thefirst one 0.6 s after injection. In the absence of inhibitor, Cy5fluorescence was maximal already at 0.6 s and remained stable for atleast 15 min. In the presence of MDM2i(1) and MDM2i(2) fluorescencedecreased slowly and measurements were made until steady-state wasachieved. Control fluorescence was taken as the difference between wellscontaining 1% DMSO and wells containing 10 μM Nutlin-3 as a control. Theinhibitory effect at each time point was calculated as percent of thecorresponding control. Progress curves obtained in the presence ofdifferent concentrations of inhibitor were combined and fitted as awhole. Nonlinear regression was performed with XLfit® using a novel fitmethodology that was designed to obtain precise K_(on) and K_(off)values, based on the following respective equations:Fit=[Imin+((Imax−(((KînH)*Imax)/((ŷnH)+(KînH))))*(1−exp(((−1)*(koff+((y*koff)/Ki)))*X)))]andFit=[Imin+((Imax−(((KînH)*Imax)/((ŷnH)+(KînH))))*(1−exp(((−1)*(kon*(Ki+y)))*X)))],where Ki represents the constant of inhibition, Imin represents theminimum inhibition (in %), Imax represents the maximum inhibition (in%), nH represents the Hill coefficient, x represents the time and yrepresents the inhibitor concentration). This data is shown in FIG. 1A.

Cell proliferation inhibition and GRIP p53 translocation assay: Effectsof MDM2i(1) and MDM2i(2) on cellular growth and loss of viability ismeasured in both p53 wild-type (SJSA-1 and HCT116 p53^(wt/wt) cells) andp53 mutant cell lines (SAOS2 and HCT116 p53^(−/−) cells) using astandard proliferation assay based on the DNA-interacting fluorescentdye YOPRO (Invitrogen: Lucern, Switzerland). Briefly, cells are platedin 96-well plates overnight at 37° C. and are treated with increasingconcentrations of MDM2i(1) or MDM2i(2) for 72 hours. Cell concentrationin each well is then determined using the DNA-interacting fluorescentdye YOPRO according to the manufacturer's instructions and thefluorescent signal is measured using a Gemini-EM standard plate reader(Molecular Devices:Sunnyvale, Calif., USA). IC₅₀ values are calculatedby curve fitting using XLfit® (Fit Model #201) and this data is shown inFIG. 1A.

The mechanistic p53-MDM2 Redistribution assay (GRIP assay) is used todirectly monitor in cells the ability of compounds to modulate thep53-MDM2 protein-protein interaction. In this fully engineered assay,the p53 protein is tagged with a fluorescent GFP-label and is bound toMDM2 protein which is anchored in the cytoplasm of the cells. Thetreatment of the cells with specific compounds causes the dissociationof the interaction between the two proteins and the translocation of thereleased p53-GFP protein from the cytoplasm to the nuclei. This effectis detected and quantified using a high content imaging platform usingthe ArrayScan-VTi (Cellomics), following the fluorescent signal overtime (see FIG. 1B, GRIP p53 translocation assay).

Altogether, using both in vitro and cellular assays, the resultspresented in FIGS. 1A and 1B show that both MDM2i(1) and MDM2i(2) arecomparable potent p53-MDM2 protein-protein interaction inhibitors invitro, inhibiting the p53-MDM2 protein-protein interaction, hamperingtumor cell proliferation in a p53-dependent manner, and inducing p53accumulation and translocation to the nucleus. This data is shown inFIG. 1B; note that there are large discrepancies in the IC50 between thecell lines. This is also an indication that there are differences in thesensitivity between the two cell lines to an MDM2i, and thus adetermination of sensitivity can be useful in determining which patientsreceive the therapeutic.

Example 2 The p53 Mutational Status is Associated with MDM2i ChemicalSensitivity in Cell Lines

The association of p53 mutation to MDM2i(2) chemical sensitivity in apanel of cancer-relevant cell lines was tested by Fisher's Exact test.The cell line panel is the one covered by the Cancer Cell LineEncyclopedia (CCLE) initiative (Barretina J., Caponigro G., Stransky N.,Venkatesan K., et al. The Cancer Cell Line Encyclopedia enablespredictive modeling of anticancer drug sensitivity. Nature 483:603-7,2012). A detailed genomic, genetic and pharmacologic characterizationwas conducted on the CCLE cell lines.

p53 mutation status in CCLE cell lines is taken from a data source ofgene-level genetic alterations, for example, point-mutations,insertions, deletions and complex genetic alterations, compiled from theSanger center COSMIC data and internal sources including Exome CaptureSequencing. This comprises data from approximately 1,600 cancer-relatedgenes over the CCLE cell lines. Analysis of the CCLE panel revealed 244cell lines containing a mutant p53, and 112 cell lines that expressedwild type p53.

The MDM2i(2) chemical sensitivity was determined from the pharmacologiccharacterization of the CCLE cell lines. The cell lines were separatedin two groups according to MDM2i(2) sensitivity. One group contains thecell lines sensitive to MDM2i(2) compound, while the other groupencompasses those being chemically insensitive to the MDM2i(2) compound.Such stratification resulted in two groups of 47 sensitive and 309insensitive cell lines, respectively. From such in vitro chemicalsensitivity data, the prediction that a cell would be sensitive to anMDM2i treatment was estimated to be 13%.

The statistical testing of the p53 mutation to sensitivity groupsassociation shows an association between p53 mutation (mt) and thechemical sensitivity to MDM2i(2) (FIG. 2). The mt panel in FIG. 2displays the MDM2i(2) sensitivity profiles for p53 mutated CCLE celllines, the wild type (wt) panel displays the MDM2i(2) sensitivityprofiles for p53 wild-type CCLE cell lines. Amax is defined as themaximal effect level (the inhibition at the highest tested MDM2i(2)concentration, calibrated to MG132, a proteaseome inhibitor used as areference, as described in the CCLE publication referenced above, andIC50 is defined as of the μM concentration at which MDM2i(2) responsereached an absolute inhibition of −50 with respect to the referenceinhibitor. Cell line count broken down by MDM2i(2) chemical sensitivityand p53 mutation status, and associated statistics: Data is alsodisplayed as a contingency table with associated statistics.

This data indicates it is more likely for a cell line to showsensitivity to MDM2i(2) if its p53 mutation status is wild type. Indeed,the majority of p53 mutated cell lines are found insensitive to thecompound, whereas more than two-third of p53 wild type cell lines aresensitive.

From this data we can conclude a p53 wild type genotype is the firstindication of MDM2i sensitivity, and therefore it is the firststratification biomarker to be considered for selecting cancer patientsresponsive to an MDM2i.

Example 3 Prediction of Cell Line Chemical Sensitivity to MDM2i fromGenomic Data and Clinical Implication

The two cell line sensitivity groups, given by MDM2i(2) treatment, arecompared with the aim of identifying the biomarkers differentiating thesensitive cell lines from the insensitive cell lines, prior to any MDM2itreatment. Such biomarkers are used to predict the sensitivity of anyMDM2i treatment. The biomarkers analyzed are the following types: 1)gene-level expression values generated by the Affymetrix GeneChip™technology with the HG-U133 plus 2 array, summarized according to theRMA normalization method; 2) gene-level chromosome copy number values,obtained with the Affymetrix SNP6.0 technology (Affymetrix Santa Clara,Calif., USA) and processed using the Affymetrix apt software, andexpressed as log 2 transformed ratios to a collection of HapMapreference normal samples; 3) gene-level genetic alterations ormutations, as described above in Example 2; 4) pathway-level expressionvalues, summarizing pathway expression levels by a standardized averageapproach over the genes contributing to the pathways, as referenced inthe GeneGo Metacore® knowledge base; 5) cell line lineage (cell linetissue of origin); 6) gene-level Tumor suppressor status, summarizingthe activation status of a selection of tumor suppressor genes, byintegrating the genetic alteration, copy number and expressioninformation. Such genomic data was generated in the context of the CCLEcell line genomic and genetic characterization, and covers a total ofabout 45,000 genomic features.

Wilcoxon signed-rank tests or Fisher's exact tests are used to comparethe two cell line group genomic features, depending on the feature type.The features having continuous values (gene expression and copy number,pathway expression features) are subjected to Wilcoxon signed-rankedtest, those having discrete values (genetic alteration, tumor suppressorstatus and lineage features), to Fisher's exact test, for differentialprofile evaluation between sensitive and insensitive cell line groups.The significant features, discriminating the sensitive cell line groupfrom the insensitive one, are the ones passing a false discoveryrate-controlled p-value cutoff. Irrespective of the p-value limit, aminimum or maximum number of features per feature type are alsorequired. To minimize the impact of the high degree of correlation amongthe features on the feature selection step, the feature data isclustered before the statistical tests as a pre-processing step. Thisstep is performed at the feature type-level using the Frey's and Dueck'sAffinity Propagation method (Clustering by Passing Messages Between DataPoints. Frey B. and Dueck D. Science 315:972-6, 2007), and retrieves aset of features representing the most variability.

The cell line sensitivity groups or classes are defined as follows forthis two-class comparison aiming at biomarker identification: asensitive group of 47 sensitive cell lines, and an insensitive group of204 insensitive cell lines. The 204 cell lines making this insensitivegroup are the most insensitive ones from the 309 insensitive cell lineset mentioned in Example 2. This feature selection step yielded a totalof about 200 significant features, having a significant differentialprofile to differentiate the sensitive cell line group from theinsensitive one, and thus having the required properties to beconsidered as markers to predict the chemical sensitivity of samples toan MDM2i. As described above in Example 2, a relevant biomarker is thep53 mutation status itself. The statistics of the feature selection step(p-value 1.17E-21) confirmed its role in predicting sensitivity to anMDM2i. Furthermore, the odds-ratio associated with p53 status (0.024)indicates p53 mutation is more represented in MDM2i insensitive celllines. Still noteworthy and still according to the statistics of thefeature selection step, most of the predictive biomarkers are found tobe a subset of p53 transcriptional target genes. These are shown inTable 2/FIG. 3. Their fold-changes indicate the transcripts of thesebiomarkers are more expressed in the MDM2i sensitive cell linepopulation, as shown in FIG. 3. This is likely indicative of a level ofp53 functional activity pre-existing before any treatment in cell linesthat are sensitive to an MDM2i.

That the biomarkers in Table 2/FIG. 3 are reflective of a functional p53pathway in MDM2i sensitive cells is verified in FIG. 4. In FIG. 4, cellshave been treated with increasing concentrations of MDM2i(1) for 4hours, prior to cell lysis. Whole cell lysates were prepared using acell lysis buffer containing 50 mM Tris-HCl pH 7.5, 120 mM NaCl, 1 mMEDTA, 6 mM EGTA pH 8.5, 1% NP-40, 20 mM NaF, 1 mM PMSF and 0.5 mMNa-Vanadat, proteins were separated on NuPAGE 4-12% Bis-Tris Gel(Invitrogen # NP0322BOX, Lucerne; Switzerland), transferred ontoNitrocellulose Protran® BA 85 membranes (Whatman #10 401 261:Piscataway, N.J., USA) at 1.5 mA/cm² membrane for 2 h using a semi-dryblotting system, and immunoblotted with either an anti-phospho-p53(Ser¹⁵) (1/1000; Cell Signaling Technology #9284: Beverly, Mass., USA)rabbit polyclonal antibody, or an anti-p53 (Ab-6) (Pantropic, cloneDO-1) (1/1000; Calbiochem # OP43 San Diego, Calif., USA), an anti-MDM2(Ab-1, clone IF2) (1/1000; Calbiochem # OP46), an anti-p21^(wAF1)(Ab-1,clone EA10) (1/500; Calbiochem # OP64: San Diego, Calif., USA) or ananti-α-Tubulin (Sigma # T5168: St. Louis, Mo., USA) mouse monoclonalantibodies, as indicated. As shown in FIG. 4, increasing concentrationsof MDM2i(1) induces stabilization of p53 protein levels, with nosignificant increase of phospho-p53 in both C3A and COLO 792 cells.Interestingly, treatment with MDM2i(1) also induces a strong de novoexpression of both p53 target genes p21(CDKN1A) and MDM2 in C3Asensitive cells, but not in COLO-792 insensitive cell line. Altogether,this data indicates that sensitivity to an MDM2i inhibitor is directlyrelated to the presence of an intrinsic functional p53 pathway, that thebiomarkers in Table2/FIG. 3, taken together as a set or alone, pointdirectly to p53 pathway functionality before treatment, and indicatesthese biomarkers have a strong ability to predict patient sensitivitythat correlates with the mechanism of action of p53.

The significant genomics features are used as the basis features fornaïve Bayes probabilistic modeling of the two MDM2i chemical sensitivitygroups, or classes. The goal of the modeling step is to derive aclassification scheme or classifier that predicts the patient's response(either sensitive or insensitive) of an unknown sample with a certainconfidence. The predictive model is defined by training a naïve Bayesalgorithm over the entire chemically characterized COLE cell linepopulation stratified into the two above-mentioned sensitivity classes.The performance of the classifier is evaluated through 5 repeats of5-fold cross-validations of the data used to train the model. The modelperformance is summarized with the following class-level measures:sensitivity, specificity, positive predictive value, and negativepredictive value. The sensitive class is used as the reference for thesensitivity and positive predicted value calculations. The defaultoutput of the naïve Bayes algorithm is a score or probability, for eachpredicted sample to be assigned to one class or the other. A probabilitythreshold is defined to transform the probability scores into asensitive or insensitive class-level prediction. The probabilitythreshold is defined as the probability maximizing the sensitivity andspecificity calculated over all predicted samples. The entire andnearly-identical procedure is described in more details in the COLEpublication referenced in Example 2.

To demonstrate a better predictive power can be achieved from at leastone biomarker found in Table 2, and alternatively, the biomarkers inTable 2 as a set, than from p53 mutation status alone, or from theentire predictive feature set of about 200 genes, naïve Bayes modelsfrom each of these feature sets were trained, and their performanceassessed by cross-validation, as above mentioned, and compared (FIG. 5).FIG. 5 demonstrates the selected biomarkers found in Table 2 outperformboth the p53 mutation status and the larger group of about 200significant features, and provide a substantial improvement inpredicting patient responsiveness to an MDM2i. This is particularlystriking when performances are evaluated by positive predictive value(PPV).

A positive predicted value (PPV) of 76% suggests that 76% of thepredicted sensitive cell lines will be sensitive to MDM2i treatment. Asan extrapolation to a clinical setting, a PPV of 76% also suggests that76% of cancer patients, predicted as sensitive to MDM2i(2) from tumorbiopsies, would show clinical response upon MDM2i(2) treatment. Thisenrichment of clinical response by patient stratification, using thebiomarkers in Table 2 and associated with the naïve Bayes predictivemodel, was compared to the baseline clinical response rate without priorpatient stratification. The baseline clinical response rate estimatedfrom the chemical sensitivity data is 19% Thus, in a clinicalperspective; the biomarkers of Table 2 have a clear increase of theclinical response in the predicted sensitive patient population. Thisincrease in prediction is greater and more specific than assaying forp53 status alone or all of the approximately 200 genomic featuresinitially selected. This can be seen in FIG. 5, where the “All 200biomarkers” feature bar is the PPV of the approximately 200 genomicfeatures initially selected. The PPV reported for “All 200 biomarkers”feature is 59%. The PPV for using p53 alone is 56% (FIG. 5). The PPV ofthe set of biomarkers disclosed in Table 2 is 76% (“Table 2 selectedbiomarkers”). This is a surprising result, as in general, the largerdata set of 200 genomic features would provide more data points and moreinsight into prediction of MDM2i sensitivity. However, this is not thecase, as the biomarkers in Table 2, when taken as a set, provide a 17%increase in predictive value. This is important in the clinic, as now 17additional patients out of 100 would be predicted to receive the correcttreatment.

In order to test the predictive value of the biomarkers of Table 2, 52cell lines that were not previously examined in the pharmacologiccharacterization as part of the COLE project, were assayed for theirsensitivity to MDM2i in proliferation assays. Briefly, cells are platedin 96-well plates overnight at 37° C. and are treated with increasingconcentrations of an MDM2i for 72 hours. Cell concentration in each wellis then determined using the CellTiter-Glo Luminescent Cell ViabilityAssay® (Promega Cat. # G7571/2/3: Madison Wis., USA), according to themanufacturer's instructions and the luminescent signal is measured usinga SYNERGY HT plate reader (BioTek: Winooski, Vt., USA). IC₅₀ values arecalculated by curve fitting using XLfit® (FIG. 6). Sensitivity of celllines to an MDM2i is determined by comparing the observed IC₅₀ of allcells that were tested in cell proliferation inhibition assay asdescribed in Example 1. The cut-off for sensitivity was determined atIC₅₀≦3 μM for both MDM2i(1) and MDM2i(2). Predictions of sensitivity forevery cell lines are performed using the predictive model as describedabove, to confirm the values disclosed in FIG. 5. The cell lines arefrom a variety of tumors. For example; melanoma (COLO-829, COLO-849,IGR-1, MEL-JUSO,SK-MEL-1, SK-MEL-31, UACC-62, UACC-257), leukemia(BV173, EOL-1, GDM-1,HuNS1, L-540, MV-4-11,OCI-LY3, RS4:11, SUP-B15,HDLM2, JM1), breast cancer (CAL51, EFM-192, HCC202), pancreatic cancer(DAN-G), hepatic cancer (JHH-5) and lung cancer (RERF-LCK-KJ). Thisassay was done with the all of the biomarkers disclosed in Table 2 as asingle set. Overall, 36/52 cell lines were predicted to be sensitive toan MDM2i, and of these 36 cell lines 24 were sensitive, resulting in apositive predictive value of 66%, again a significant increase inpredictive value (PPV) over assaying for p53 alone. With regard toscreening out the non-responding cells, 16/52 cell lines were predictedto be insensitive to a p53-MDM2 inhibitor, and 13/16 were found toindeed be insensitive, leading to a significant negative predictivevalue (NPV) of 81%. Overall, these data are similar to the predictivemodel performances described in FIG. 5. The actual in vitro testing ofunrelated cell lines allowed testing of the MDM2i chemical sensitivitypredictive model, hence validating the biomarkers disclosed in Table 2.

Example 4 MDM2i Treatment Inhibits Tumor Growth Inhibition In Vivo whichis Correlated to a Dose-Dependent Increase of p21(CDKN1A) mRNA andProtein Levels in Tumors

To further validate the predictive biomarkers disclosed in FIG. 3, invivo human xenograft models either from human primary samples or fromcell lines were directly injected and grown in tumors subcutaneously inmice and then assessed for MDM2i sensitivity. All the animals wereallowed to adapt for 4 days and housed in a pathogen-controlledenvironment (5 mice/Type III cage) with access to food and water adlibitum. Animals were identified with transponders. Studies wereperformed according to procedures covered by permit number 1975 issuedby the Kantonales Veterinäramt Basel-Stadt and strictly adhered to theEidgenössisches Tierschutzgesetz and the EidgenössischeTierschutzverordnung. Subcutaneous tumors were induced by concentrating3.0×10⁶ SJSA-1 osteosarcoma cells in 100 μl of PBS (without Ca²⁺ andMg²⁺) and injecting in the right flank of Harlan nude mice. Theadministration of MDM2i began 12-14 days post cell injection. MDM2i wasprepared immediately before each administration. MDM2is were dissolvedin 0.5% HPMC (hydroxypropylmethylcellulose) and were injected daily (q24h) at 25, 50 or 100 mg/kg. Tumor volumes (TVol), determined from calipermeasurements (using the formula l×w×h×π/6) were measured three times perweek. Tumor response was quantified by the change in tumor volume(endpoint minus starting value in mm³) as the T/C, i.e.

$\left( {\frac{\Delta \; {TVol}_{drug}}{\Delta \; {TVol}_{vehicle}} \times 100} \right).$

In the case of a tumor regression, the tumor response was quantified bythe percentage of regression of the starting TVol, i.e.

$\left( {\frac{\Delta \; {TVol}_{drug}}{\Delta \; {TVol}_{{Day}\; 0}} \times 100} \right).$

The body-weight (BW) of the mice was measured three times per weekallowing calculation at any particular time-point relative to the day ofinitiation of treatment (day 0) of both the percentage change in BW (Δ%BW). As shown in FIG. 7A, a 10-day treatment of SJSA-1 xenograftedtumors with MDM2i(1) led to a dose-dependent tumor growth inhibitionwith a significant T/C of 50% at 25 mg/kg q24 h and of 3% (stasis) at 50mg/kg q24 h. At 100 mg/kg q24 h for 10 days, MDM2i(1) treatment induceda significant tumor regression of 65% (FIG. 7B). All doses were welltolerated at q24 h schedule, as indicated by the mean body weight curvesover time.

Anti-tumor activity of MDM2i(1) was correlated with a significantdose-dependent induction of p21(CDKN1A) mRNA levels in tumors (FIG. 7C).Briefly, total RNA was purified from cell pellets using the QIAshredder®(79654, Qiagen:Valencia Calif., USA) and RNeasy Mini Kite (74106,Qiagen: Valencia Calif., USA) according to the manufacturer'sinstructions, with the exception that no DNA digestion was performed.Total RNA was eluted with 50 μL of RNase-free water. Total RNA wasquantitated using the spectrophotometer ND-1000 Nanodrop® (WilmingtonDel., USA). The qRT-PCR (Quantitative Reverse Transcriptase PolymeraseChain Reaction) was set up in triplicate per sample using the One-StepRT qPCR Master Mix Plus (RT-QPRT-032X, Eurogentec: Seraing, Belgium),with either control primers and primers for human p21(CDKN1A)(Hs00355782_m1, Applied Biosystems: Carlsbad Calif., USA) or mousep21(CDKN1A) (Mm00432448_m1, Applied Biosystems: Carlsbad Calif., USA),namely TaqMan Gene Expression kit assays (20× probe dye FAM™ (orVIC)-TAMRA (or MGB); Applied Biosystems: Carlsbad Calif., USA). Morespecifically, a master mix was prepared on ice for a final concentrationof: 1× Master Mix buffer, 1× primer solution, and 1× Euroscript reversetranscriptase, combined with H₂O, total volume: 8 μL/well. A MicroAmpOptical 384-well Reaction Plate (4309849, Applied Biosystems) was fixedon the bench, and 2 μL of mRNA (concentration: 10 or 20 ng/μl) (or waterfor negative control) were pipetted in triplicate, followed by additionof 8 μL/well of master mix. The plate was then covered with a MicroAmpOptical Adhesive film kit (4313663, Applied Biosystems: Carlsbad Calif.,USA), centrifuged for 5 min at 1000 rpm at 4° C. and placed in a 7900 HTFast Real-Time PCR System (Applied Biosystems: Carlsbad Calif., USA).The program was run with one cycle of 48° C. for 30 min, one cycle of95° C. for 10 min, and finally 40 cycles of alternating 95° C. for 15sec and 60° C. for 1 min. The number of cycles (CT) was determined,2^(−CT) values were calculated, and the value normalized by dividingwith the 2^(−CT) value obtained from the Gapdh control. Fold increaseover control (i.e. DMSO- or vehicle-treated animals) was calculated andplotted in the bar graph.

In addition, the anti-tumor activity of MDM2i(1) was correlated with asignificant dose-dependent induction of p21(CDKN1A) protein levels intumors, as judged by immunohistochemistry (FIG. 8). SJSA-1 xenografttumors were collected and a 3-4 mm slice out of the middle of the tumorwas removed, transferred into pre-labelled histo-cassettes andimmersion-fixed in neutral buffered formalin (NBF) 10% (v/v) (pH6.8-7.2) (J. T. Baker, Winter Garden, Fla., USA), pre-cooled at 4° C.Tumors were then fixed at room temperature for 24 hours, followed byprocessing in the TPC 15Duo (Tissue Processing Center, Medite) forparaffinization. Subsequently, the tumor slices were embedded inparaffin and from each paraffin block several 3 μm thick sections werecut on a rotary microtome (Mikrom International AG, Switzerland), spreadin a 48° C. water-bath, mounted on glass slides (SuperFrost Plus, ThermoScientific:Waltham Mass., USA), and dried in an oven either at 37° C.overnight or at 60° C. for 30 min. Dry tissue section were processed forimmunohistochemistry (IHC) staining. p21(CDKN1A) immunohistochemistryhas been performed using the mouse monoclonal antibody clone SX118 fromDako (Cat. No. M7202 Dako: Carpenteria Calif., USA) at a dilution of1:50. Immunohistochemistry has been performed on a Ventana Discovery XTautomated immunostainer using the N-Histofine Mousestain Kit (NichireiBioscience Inc, Japan) in combination with the DABMap Kit chromogensystem, omitting the SA-HRP solution (Ventana/Roche Diagnostics GmbH,Mannheim, Germany). Antigen retrieval was done by using CellConditioning ULTRA® (Ventana/Roche Diagnostics GmbH, Mannheim, Germany)at mild (95° C. for 8 min+100° C. for 20 min) conditions. Mouse crossreactivities were blocked by using Blocking Reagents A and B from theN-Histofine Mousestain Kite (Nichirei Bioscience Inc, Japan) before andafter primary antibody incubation, following the manufacturerinstructions. The primary antibody was applied manually at the desireddilution in Dako antibody diluent (AbD), followed by incubation for 1hour at ambient temperature. Corresponding negative controls wereincubated with AbD only. Sections were subsequently stained using thelabeled polymer system Simple Stain Mouse MAX PO (M) from theN-Histofine Mousestain Kite (Nichirei) and DAB substrate from the DABMapKit (Ventana/Roche Diagnostics). Counterstaining of sections was doneusing hematoxylin (Ventana/Roche Diagnostics). After the automatedstaining run, slides were dehydrated in a graded series of ethanol,cleared in xylene and mounted with Pertex® mounting medium.

Example 5 Prediction of MDM2i(2) Sensitivity in Human Primary TumorMouse Xenograft Models and in Human Primary Tumors

The biomarkers in Table 2, were used in association with a naïve Bayespredictive model to predict MDM2i sensitivity in a collection of humanprimary tumor samples and xenograft models, to demonstrate whether thebiomarkers, and their associated predictive power, exists outside of invitro cell line systems.

The gene-level expression values of all the biomarkers of Table 2 wereused as the feature basis for naïve Bayes probabilistic modeling. Theywere generated as described in Example 3, the only difference being inthe RMA summarization step where the normalization was targeted to areference set of normal & tumor samples. The naïve Bayes modeling isconducted as described in Example 3.

The human primary tumor samples and xenograft models submitted forsensitivity prediction were a collection of about 18,000 and 503samples, respectively, for which gene expression profiles, generatedwith Affymetrix technology (Human Genome U133 plus 2.0 array), areavailable. The samples of the collection were internally annotated withcontrolled vocabulary for sample ontology including pathology, histologyand primary site. The associated gene chip data was gathered from bothpublic and internal sources, and normalized as described above to thesame reference sample set for consistency.

Ratios of MDM2i predicted sensitive samples from the collection werecompared to the proportions of sensitive cell lines, as given by theMDM2i(2) chemical sensitivity data described in Example 3 above. A goodcorrelation is expected to demonstrate the ability of the biomarkersdisclosed in FIG. 3 to be predictive for MDM2i sensitivity in humanprimary tumor samples. For clarity, as well as to potentially identifylineages in which sensitive cell line proportions are underestimated inthe cell line chemical sensitivity data, sensitive prediction ratio tosensitive cell line ratio comparison is broken down by tissue of origin.

FIG. 9 (left panel) shows a correlation between predicted sensitivehuman primary tumor samples from the collection and the sensitive celllines from MDM2i chemical sensitivity data. It indicates the biomarkersdisclosed in Table 2 and its use for predicting sensitivity outside ofin vitro cell line samples is valid. It also indicates that thebiomarkers disclosed in Table 2 can be used to predict MDM2i chemicalsensitivity in human primary tumor samples. It reveals new tumorindications which have not been investigated previously and confirmsresults found in the current study The new indications, for example,liver (hepatocellular carcinoma) and kidney (renal cell carcinoma),represent potentially new disease indications to be pre-clinically andclinically evaluated with the biomarkers disclosed in Table 2 fortreatment with an MDM2i. FIG. 9A also indicates that the biomarkersdisclosed in Table 2 can be used to predict MDM2i chemical sensitivityin primary melanoma tumors, consistent with the results found in thecurrent study.

FIG. 9 (right panel) shows a correlation between the fractions ofpredicted sensitive human primary tumor samples and the predictedsensitive ratios in the primary tumor xenograft collection. The tumorsamples/xenografts/cell lines are organized by lineage. The dashed linein both panels is the identity line. It shows the data generated fromthe in vivo mouse xenograft models, in which the exemplified signatureand associated predictive classifier can be studied and validated, is inline with the data from the rest of the in vivo collection samples. Itconfirms the mouse xenograft models as a source of material to validatethe p53 downstream target gene based classifier approach to predictclinical outcome of cancer patients and diseases indications, in an invivo pre-clinical setting.

Example 6 Single Biomarkers and any Combinations of the IdentifiedThirteen Biomarkers Predict Chemical Sensitivity to MDM2i

The thirteen biomarkers depicted in Table 2, when used in associationwith a naïve Bayes predictive modeling framework, predict MDM2isensitivity in both in vitro systems and in vivo, as exemplified inExamples 3 and 5. To investigate whether subsets of these thirteenbiomarkers would also predict for MDM2i sensitivity, single biomarkersand multiple combinations of them are employed as feature basis forpredictive modeling. Their prediction performances are then compared tothe ones achieved with either the full thirteen biomarkers or with p53mutation status when used as a predictive feature for MDM2i sensitivityprediction.

Two instances of p53 mutation status are considered in Example 6. Thesetwo instances are defined from the Exome Capture Sequencing data of theCCLE cell lines, as mentioned in Example 2, and are meant to besurrogates of clinical settings where the p53 gene is sequenced forstratification or clinical annotation of patients.

The first instance of p53 mutation status is defined from the mutationsspanning exons 5 to 8 of p53. Exons 5 to 8 encompass the DNA bindingdomain of p53, which contains the majority of described p53 mutations,and are the p53 exons commonly targeted for sequencing in clinicalsettings (for example, Rapid sequencing of the p53 gene with a newautomated DNA sequencer. Bharaj B., Angelopoulou K., and Diamandis E.,Clinical Chemistry 44:7 1397-1403, 1998). The second instance considersthe complete open reading frame of the main p53 transcript, and istherefore defined from all coding exon mutations.

Multiple biomarker combinations can be generated from the list of 13biomarkers disclosed in Table 2. All combination types from 2 to 12biomarkers are evaluated as feature basis for predictive modeling ofMDM2i chemical sensitivity. When more than 50 different combinationsexist for a given combination type, the number of evaluated combinationsis restricted to 50. All 50 combinations in each combination type wererandomly picked.

All predictive models associated to the above described feature sets(single biomarkers, 2-to-12 biomarker combinations, p53 mutation statusinstances) were trained and evaluated mostly as described in Example 3.What differs from Example 3 is as follows: The training data wasslightly larger than the one used is Example 3 and encompasses 264 celllines (47 from the sensitive class, and 217 from the insensitive class);A p-value threshold of 0.5 was used upon the naïve Bayes probabilisticmodeling to call a cell line either sensitive or insensitive in the5-fold cross-validation scheme. Moreover, all sample strata generated bythe cross-validation processes were randomly selected and independentfrom one-another. The performances of the combinatorial predictivemodels and their comparisons to the p53 mutation status instance and 13biomarker models are shown in FIGS. 10 to 12.

FIG. 10 depicts the positive predicted values (PPV) achieved by thesingle biomarker, the combinatorial, the thirteen biomarker and the p53mutation models. The PPV is an estimate of the clinical efficacy onewould expect in a clinical trial upon patient selection with theconsidered modeling process. For convenience, the data is depicted asbox-and-whisker plots when there are more than five data points to plotper feature set.

FIG. 10 shows that combinations from as few as two and three biomarkersoutperform exon 5-to-8 p53 mutation (“ex5to8mt”) and all-exon p53mutation features (“allExMt”), respectively. Indeed, the upper and lowerboundaries defined by the ends of the whiskers encompass about 99% ofthe data points, assuming a normal distribution of the data. Therefore,the majority of the evaluated 2- and 3-biomarker combinations show ahigher PPV than the ones achieved by the p53 mutation status instances.Moreover, even if all single gene models do not outperform the two p53mutations instances, a majority of them (around 75%, the box plus theupper whisker) outperforms the p53 all-exon mutations. Noteworthy, allsingle gene models give rise to PPVs that are higher than the sensitivecell line ratio (-18%) in the considered sample population, indicatingthat MDM2i sensitivity prediction models, built from as few as onebiomarker, are capable of enriching the selected samples in sensitiveones.

Additionally, under this modeling exercise, as plotted in FIG. 10, thePPV given by the p53 exons 5-to-8 mutation status model, averaged overthe 5 cross-validation repeats, is 48%. It is significantly lower thanthe p53 mutation PPV disclosed in Example 3 (56%). This indicates that,in a clinical setting where p53 exon 5-to-8 sequencing is employed forpatient selection, which is common practice, the exemplified 13biomarker-based patient selection has even higher added value thananticipated from Example 3.

FIG. 11 shows the specificities achieved by the several evaluatedmodels. As for PPV in FIG. 10, every combination made from as few as 2biomarkers is sufficient to achieve specificity higher than the onesobtained from the mutations instances only. All single biomarker modelsoutperform the mutations, when specificity is used to monitor the modelperformances.

FIG. 12 shows the sensitivities. Sensitivity is also called recall, andis an estimate of the truly sensitive patient population retained uponpatient selection. Combinations of 9 biomarkers as the feature basis forMDM2i sensitivity prediction models are sufficient to obtainsensitivities comparable to the one achieved the full 13 biomarker list.However, only a few 9-biomarker combinations would achieve sensitivitieshigher than the ones given by the 2 p53 mutation status predictivemodels. But noteworthy, all evaluated combinations, from as few as 2biomarkers, and a majority of single biomarker models, displaysensitivities higher than the one which is expected by chance uponrandom classification (˜18%).

In conclusion from FIGS. 10, 11 and 12, single biomarkers, when used asfeature basis in models predicting chemical sensitivity to MDM2i, aresufficient to achieve sample sensitivity predictions that would resultin a significant enrichment of potentially MDM2i responding patient in aclinical setting. Furthermore, combinations made from any 2 biomarkers,or more, increase the expected clinical efficacy with respect to the oneobtained with p53 mutation-based patient selection. Assembling 8biomarkers, from any of the Table 2 thirteen ones, are sufficient toachieve a patient recall equivalent to the one given by the 13 biomarkermodel. The predictive model performance metrics, obtained for the 13gene signature and associated combinations, can be further optimized byoptimizing the class assignment p-value threshold used in the naïveBayes probabilistic step of the model, as it was done in Example 3.

In a further embodiment, it is investigated whether the biomarkersdepicted in Table 2 could predict MDM2i chemical sensitivity incollaboration with p53 mutation status. Single biomarkers andcombinations of 2 biomarkers and above are combined with p53 mutationstatus in feature lists. The feature lists are then utilized as basisfor sensitivity predictive modeling, as previously and described above.The performances of the multiple resulting models are evaluated asdescribed above.

The p53 mutation status instance which is used as an example is the p53exon 5-to-8 mutations. FIGS. 13, 14 and 15 depict the PPVs,specificities and sensitivities of those models combining p53 mutationwith biomarkers, respectively, and are compared to the results given bymutation only models and the full 13-biomarker model.

FIG. 13 shows that at least a single biomarker from the list of 13, incollaboration with p53 mutation status, is sufficient to achieve a PPVhigher that the basal sensitivity rate (18%) in the data. It also showsthat a single biomarker at minimum, still in combination with p53mutation status, achieves a higher PPV than the ones obtained with thetwo above mentioned p53 mutation status instances, when employed asfeatures in a predictive model. And finally, any 5 biomarkers incombination with p53 mutation recapitulate the PPV which is achieved bythe 13 biomarker model.

The same conclusions are drawn from FIGS. 14 and 15 when specificitiesand sensitivities are taken into account as model performance metrics.Noteworthy from FIG. 15, a high sensitivity can be obtained from as fewas one biomarker, when modeled along with p53 mutation.

In conclusion, combining a single or multiple biomarkers from Table 2with p53 mutation status enables the prediction of MDM2i sensitivity,and would result, when applied for patient selection in a therapeutic orclinical setting, in a significant enrichment with a limited loss ofpotential MDM2i responding patients.

1. A method of predicting the sensitivity of a cancer patient fortreatment with a Human Double Minute 2 inhibitor (MDM2i), the methodcomprising: a) measuring differential gene expression of at least onebiomarker selected from Table 2 in a cancer sample obtained from thepatient; and b) comparing the differential gene expression of the atleast one biomarker with gene expression of said biomarker in a controlsample, wherein the increase or decrease in gene expression comparisonindicates that the patient is sensitive to treatment with an MDM2i. 2.The method of claim 1, wherein more than one biomarker is selected fromTable
 2. 3. The method of claim 1, comprising the biomarkers MDM2,CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX, RRM2B, SESN1, CCNG1, XPC,TNFRSF10B and AEN.
 4. The method of claim 1, wherein comparing thedifferential gene expression of the at least one biomarker with geneexpression of a control sample indicates a functional p53 gene pathway.5. The method of claim 1, wherein the cancer sample is selected from thegroup consisting of breast, lung, pancreas, ovary, central nervoussystem (CNS), endometrium, stomach, large intestine, colon, esophagus,bone, urinary tract, hematopoietic, lymphoid, liver, skin, melanoma,kidney, soft tissue sarcoma and pleura.
 6. The method of claim 1,wherein a nucleic acid or protein of at least one biomarker is measured.7. The method of claim 1, wherein the expression of the at least onebiomarker is increased in the cancer sample when compared to a controlsample.
 8. The method of claim 1, wherein the MDM2i is selected fromTable
 1. 9.-16. (canceled)
 17. A method of predicting the sensitivity ofa cancer cell to a Human Double Minute 2 inhibitor (MDM2i), the methodcomprising: a) obtaining a cancer sample from a cancer patient, b)measuring differential gene expression of at least two biomarkersselected from Table 2 in the cell; and c) comparing the differentialgene expression of the at least two biomarkers selected from Table 2with gene expression of the at least two biomarkers from a normal orcontrol cell.
 18. The method of claim 17, wherein the MDM2i is selectedfrom Table
 1. 19. The method of claim 17, wherein more than threebiomarkers are selected from Table
 2. 20. The method of claim 17,comprising the biomarkers MDM2, CDKN1A, ZMAT3, DDB2, FDXR, RPS27L, BAX,RRM2B, SESN1, CCNG1, XPC, TNFRSF10B and AEN.
 21. The method of claim 17,wherein comparing the differential gene expression of the at least twobiomarkers with gene expression of a control sample indicates afunctional p53 gene pathway.
 22. The method of claim 17, wherein thecancer sample is selected from the group consisting of breast, lung,pancreas, ovary, central nervous system (CNS), endometrium, stomach,large intestine, colon, esophagus, bone, urinary tract, hematopoietic,lymphoid, liver, skin, melanoma, kidney, soft tissue sarcoma and pleura.23. The method of claim 17, wherein a nucleic acid or protein of atleast two biomarkers is measured.
 24. The method of claim 17, whereinthe gene expression of the at least two biomarkers is increased in thecancer cell. 25.-48. (canceled)